Module Awso_sagemaker_syncSource

Sourceval create_device_fleet : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.CreateDeviceFleetRequest.t -> (unit, unit) Result.t
Sourceval create_edge_deployment_stage : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.CreateEdgeDeploymentStageRequest.t -> (unit, unit) Result.t
Sourceval create_edge_packaging_job : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.CreateEdgePackagingJobRequest.t -> (unit, unit) Result.t
Sourceval create_hub_content_presigned_urls : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.CreateHubContentPresignedUrlsRequest.t -> (Awso_sagemaker.Values.CreateHubContentPresignedUrlsResponse.t, Awso_sagemaker.Values.CreateHubContentPresignedUrlsResponse.error) Result.t
Sourceval create_hub_content_reference : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.CreateHubContentReferenceRequest.t -> (Awso_sagemaker.Values.CreateHubContentReferenceResponse.t, Awso_sagemaker.Values.CreateHubContentReferenceResponse.error) Result.t
Sourceval delete_algorithm : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteAlgorithmInput.t -> (unit, unit) Result.t
Sourceval delete_app : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteAppRequest.t -> (unit, unit) Result.t
Sourceval delete_app_image_config : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteAppImageConfigRequest.t -> (unit, unit) Result.t
Sourceval delete_cluster_scheduler_config : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteClusterSchedulerConfigRequest.t -> (unit, unit) Result.t
Sourceval delete_code_repository : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteCodeRepositoryInput.t -> (unit, unit) Result.t
Sourceval delete_compilation_job : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteCompilationJobRequest.t -> (unit, unit) Result.t
Sourceval delete_compute_quota : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteComputeQuotaRequest.t -> (unit, unit) Result.t
Sourceval delete_data_quality_job_definition : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteDataQualityJobDefinitionRequest.t -> (unit, unit) Result.t
Sourceval delete_device_fleet : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteDeviceFleetRequest.t -> (unit, unit) Result.t
Sourceval delete_domain : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteDomainRequest.t -> (unit, unit) Result.t
Sourceval delete_edge_deployment_plan : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteEdgeDeploymentPlanRequest.t -> (unit, unit) Result.t
Sourceval delete_edge_deployment_stage : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteEdgeDeploymentStageRequest.t -> (unit, unit) Result.t
Sourceval delete_endpoint : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteEndpointInput.t -> (unit, unit) Result.t
Sourceval delete_endpoint_config : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteEndpointConfigInput.t -> (unit, unit) Result.t
Sourceval delete_feature_group : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteFeatureGroupRequest.t -> (unit, unit) Result.t
Sourceval delete_hub : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteHubRequest.t -> (unit, unit) Result.t
Sourceval delete_hub_content : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteHubContentRequest.t -> (unit, unit) Result.t
Sourceval delete_hub_content_reference : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteHubContentReferenceRequest.t -> (unit, unit) Result.t
Sourceval delete_hyper_parameter_tuning_job : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteHyperParameterTuningJobRequest.t -> (unit, unit) Result.t
Sourceval delete_inference_component : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteInferenceComponentInput.t -> (unit, unit) Result.t
Sourceval delete_model : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteModelInput.t -> (unit, unit) Result.t
Sourceval delete_model_bias_job_definition : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteModelBiasJobDefinitionRequest.t -> (unit, unit) Result.t
Sourceval delete_model_card : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteModelCardRequest.t -> (unit, unit) Result.t
Sourceval delete_model_explainability_job_definition : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteModelExplainabilityJobDefinitionRequest.t -> (unit, unit) Result.t
Sourceval delete_model_package : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteModelPackageInput.t -> (unit, unit) Result.t
Sourceval delete_model_package_group : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteModelPackageGroupInput.t -> (unit, unit) Result.t
Sourceval delete_model_package_group_policy : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteModelPackageGroupPolicyInput.t -> (unit, unit) Result.t
Sourceval delete_model_quality_job_definition : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteModelQualityJobDefinitionRequest.t -> (unit, unit) Result.t
Sourceval delete_monitoring_schedule : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteMonitoringScheduleRequest.t -> (unit, unit) Result.t
Sourceval delete_notebook_instance : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteNotebookInstanceInput.t -> (unit, unit) Result.t
Sourceval delete_notebook_instance_lifecycle_config : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteNotebookInstanceLifecycleConfigInput.t -> (unit, unit) Result.t
Sourceval delete_optimization_job : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteOptimizationJobRequest.t -> (unit, unit) Result.t
Sourceval delete_processing_job : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteProcessingJobRequest.t -> (unit, unit) Result.t
Sourceval delete_project : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteProjectInput.t -> (unit, unit) Result.t
Sourceval delete_space : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteSpaceRequest.t -> (unit, unit) Result.t
Sourceval delete_studio_lifecycle_config : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteStudioLifecycleConfigRequest.t -> (unit, unit) Result.t
Sourceval delete_training_job : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteTrainingJobRequest.t -> (unit, unit) Result.t
Sourceval delete_user_profile : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeleteUserProfileRequest.t -> (unit, unit) Result.t
Sourceval deregister_devices : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DeregisterDevicesRequest.t -> (unit, unit) Result.t
Sourceval describe_hub_content : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.DescribeHubContentRequest.t -> (Awso_sagemaker.Values.DescribeHubContentResponse.t, Awso_sagemaker.Values.DescribeHubContentResponse.error) Result.t
Sourceval import_hub_content : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.ImportHubContentRequest.t -> (Awso_sagemaker.Values.ImportHubContentResponse.t, Awso_sagemaker.Values.ImportHubContentResponse.error) Result.t
Sourceval list_hub_content_versions : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.ListHubContentVersionsRequest.t -> (Awso_sagemaker.Values.ListHubContentVersionsResponse.t, Awso_sagemaker.Values.ListHubContentVersionsResponse.error) Result.t
Sourceval register_devices : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.RegisterDevicesRequest.t -> (unit, unit) Result.t
Sourceval start_edge_deployment_stage : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.StartEdgeDeploymentStageRequest.t -> (unit, unit) Result.t
Sourceval start_monitoring_schedule : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.StartMonitoringScheduleRequest.t -> (unit, unit) Result.t
Sourceval start_notebook_instance : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.StartNotebookInstanceInput.t -> (unit, unit) Result.t
Sourceval stop_auto_m_l_job : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.StopAutoMLJobRequest.t -> (unit, unit) Result.t
Sourceval stop_compilation_job : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.StopCompilationJobRequest.t -> (unit, unit) Result.t
Sourceval stop_edge_deployment_stage : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.StopEdgeDeploymentStageRequest.t -> (unit, unit) Result.t
Sourceval stop_edge_packaging_job : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.StopEdgePackagingJobRequest.t -> (unit, unit) Result.t
Sourceval stop_hyper_parameter_tuning_job : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.StopHyperParameterTuningJobRequest.t -> (unit, unit) Result.t
Sourceval stop_inference_recommendations_job : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.StopInferenceRecommendationsJobRequest.t -> (unit, unit) Result.t
Sourceval stop_labeling_job : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.StopLabelingJobRequest.t -> (unit, unit) Result.t
Sourceval stop_monitoring_schedule : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.StopMonitoringScheduleRequest.t -> (unit, unit) Result.t
Sourceval stop_notebook_instance : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.StopNotebookInstanceInput.t -> (unit, unit) Result.t
Sourceval stop_optimization_job : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.StopOptimizationJobRequest.t -> (unit, unit) Result.t
Sourceval stop_processing_job : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.StopProcessingJobRequest.t -> (unit, unit) Result.t
Sourceval stop_training_job : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.StopTrainingJobRequest.t -> (unit, unit) Result.t
Sourceval stop_transform_job : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.StopTransformJobRequest.t -> (unit, unit) Result.t
Sourceval update_device_fleet : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.UpdateDeviceFleetRequest.t -> (unit, unit) Result.t
Sourceval update_devices : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.UpdateDevicesRequest.t -> (unit, unit) Result.t
Sourceval update_feature_metadata : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.UpdateFeatureMetadataRequest.t -> (unit, unit) Result.t
Sourceval update_hub_content : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.UpdateHubContentRequest.t -> (Awso_sagemaker.Values.UpdateHubContentResponse.t, Awso_sagemaker.Values.UpdateHubContentResponse.error) Result.t
Sourceval update_hub_content_reference : ?endpoint_url:string -> ?cfg:Awso.Cfg.t -> Awso_sagemaker.Values.UpdateHubContentReferenceRequest.t -> (Awso_sagemaker.Values.UpdateHubContentReferenceResponse.t, Awso_sagemaker.Values.UpdateHubContentReferenceResponse.error) Result.t
include module type of struct include Awso_sagemaker.Values end
include module type of struct include Awso_sagemaker.Values_0 end
Sourceval service : Awso.Service.t
Sourceval apiVersion : string
Sourceval endpointPrefix : string
Sourceval serviceFullName : string
Sourceval signatureVersion : string
Sourceval protocol : string
Sourceval globalEndpoint : string
Sourceval serviceAbbreviation : string
Sourceval targetPrefix : string
Sourceval simple_to_json : ('a -> Awso__Botodata.value) -> 'a -> Yojson.Safe.t
Sourceval composed_to_json : ('a -> Awso__Botodata.value) -> 'a -> Yojson.Safe.t
Sourceval to_query : ('a -> Awso.Client.Query.value) -> 'a -> Awso.Client.Query.t
Sourceval structure_to_value_aux : ('a * 'b option) list -> f:(('a * 'b) list -> 'c) -> [> `Structure of 'c ]
Sourceval structure_to_value : ('a * 'b option) list -> [> `Structure of ('a * 'b) list ]
Sourceval structure_to_wrapped_value : wrapper:'a -> response:'a -> ('b * 'c option) list -> [> `Structure of ('a * [> `Structure of ('b * 'c) list ]) list ]
Sourcemodule String_ = Awso_sagemaker.Values.String_
Sourcemodule AIBenchmarkInferenceComponent = Awso_sagemaker.Values.AIBenchmarkInferenceComponent

An inference component to benchmark.

Sourcemodule AIBenchmarkInferenceComponentList = Awso_sagemaker.Values.AIBenchmarkInferenceComponentList

The SageMaker endpoint configuration for benchmarking.

Summary information about an AI benchmark job.

Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.

The network configuration for an AI benchmark job.

The output configuration for an AI benchmark job.

CloudWatch log information for an AI benchmark or recommendation job.

The output result of an AI benchmark job, including the Amazon S3 location and CloudWatch log information.

The target for an AI benchmark job. This is a union type — specify one of the members.

Sourcemodule AICapacityReservationPreference = Awso_sagemaker.Values.AICapacityReservationPreference
Sourcemodule AICapacityReservationConfig = Awso_sagemaker.Values.AICapacityReservationConfig

The capacity reservation configuration for an AI recommendation job.

The Amazon S3 data source for an AI workload.

The data source for an AI workload input data channel.

A channel of input data for an AI workload configuration. Each channel has a name and a data source.

Sourcemodule AIWorkloadInputDataConfigList = Awso_sagemaker.Values.AIWorkloadInputDataConfigList

The dataset configuration for an AI workload. This is a union type — specify one of the members.

Sourcemodule AIInferenceSpecificationName = Awso_sagemaker.Values.AIInferenceSpecificationName

The Amazon S3 model source configuration.

The source of the model for an AI recommendation job. This is a union type.

Sourcemodule AIRecommendationPerformanceMetric = Awso_sagemaker.Values.AIRecommendationPerformanceMetric

An expected performance metric for a recommendation.

Sourcemodule AIRecommendationOptimizationType = Awso_sagemaker.Values.AIRecommendationOptimizationType
Sourcemodule AIRecommendationOptimizationConfigMap = Awso_sagemaker.Values.AIRecommendationOptimizationConfigMap
Sourcemodule AIRecommendationOptimizationDetail = Awso_sagemaker.Values.AIRecommendationOptimizationDetail

Details about an optimization technique applied in a recommendation.

Sourcemodule AIRecommendationOptimizationDetailList = Awso_sagemaker.Values.AIRecommendationOptimizationDetailList
Sourcemodule AIRecommendationInstanceType = Awso_sagemaker.Values.AIRecommendationInstanceType
Sourcemodule AIRecommendationInstanceCount = Awso_sagemaker.Values.AIRecommendationInstanceCount
Sourcemodule AIRecommendationCopyCountPerInstance = Awso_sagemaker.Values.AIRecommendationCopyCountPerInstance
Sourcemodule AIRecommendationInstanceDetail = Awso_sagemaker.Values.AIRecommendationInstanceDetail

Instance details for a recommendation.

Sourcemodule AIRecommendationInstanceDetailList = Awso_sagemaker.Values.AIRecommendationInstanceDetailList
Sourcemodule AIRecommendationModelDetails = Awso_sagemaker.Values.AIRecommendationModelDetails

Details about the model package in a recommendation.

Sourcemodule AIRecommendationDeploymentS3Channel = Awso_sagemaker.Values.AIRecommendationDeploymentS3Channel

An Amazon S3 data channel for a recommended deployment configuration, containing model artifacts or optimized model outputs.

Sourcemodule AIRecommendationDeploymentS3ChannelList = Awso_sagemaker.Values.AIRecommendationDeploymentS3ChannelList
Sourcemodule AIRecommendationDeploymentConfiguration = Awso_sagemaker.Values.AIRecommendationDeploymentConfiguration

The deployment configuration for a recommendation.

An optimization recommendation generated by an AI recommendation job.

Sourcemodule AIRecommendationAllowOptimization = Awso_sagemaker.Values.AIRecommendationAllowOptimization
Sourcemodule AIRecommendationInstanceTypeList = Awso_sagemaker.Values.AIRecommendationInstanceTypeList
Sourcemodule AIRecommendationComputeSpec = Awso_sagemaker.Values.AIRecommendationComputeSpec

The compute resource specification for an AI recommendation job.

Sourcemodule AIRecommendationConstraint = Awso_sagemaker.Values.AIRecommendationConstraint

A performance constraint for an AI recommendation job.

Sourcemodule AIRecommendationConstraintList = Awso_sagemaker.Values.AIRecommendationConstraintList
Sourcemodule AIRecommendationInferenceFramework = Awso_sagemaker.Values.AIRecommendationInferenceFramework
Sourcemodule AIRecommendationInferenceSpecification = Awso_sagemaker.Values.AIRecommendationInferenceSpecification

The inference framework for an AI recommendation job.

Sourcemodule AIRecommendationJobSummary = Awso_sagemaker.Values.AIRecommendationJobSummary

Summary information about an AI recommendation job.

Sourcemodule AIRecommendationJobSummaryList = Awso_sagemaker.Values.AIRecommendationJobSummaryList
Sourcemodule AIRecommendationOutputConfig = Awso_sagemaker.Values.AIRecommendationOutputConfig

The output configuration for an AI recommendation job.

Sourcemodule AIRecommendationOutputResult = Awso_sagemaker.Values.AIRecommendationOutputResult

The output configuration for an AI recommendation job, including the S3 location for results and the model package group for deployment.

Sourcemodule AIRecommendationPerformanceTarget = Awso_sagemaker.Values.AIRecommendationPerformanceTarget

The performance targets for an AI recommendation job.

Summary information about an AI workload configuration.

Sourcemodule AIWorkloadConfigSummaryList = Awso_sagemaker.Values.AIWorkloadConfigSummaryList

The workload specification for benchmark tool configuration. Provide an inline YAML or JSON string.

The benchmark tool configuration for an AI workload.

Sourcemodule AcceleratorPartitionConfigCountInteger = Awso_sagemaker.Values.AcceleratorPartitionConfigCountInteger
Sourcemodule AcceleratorPartitionConfig = Awso_sagemaker.Values.AcceleratorPartitionConfig

Configuration for allocating accelerator partitions.

Sourcemodule ComputeQuotaResourceConfig = Awso_sagemaker.Values.ComputeQuotaResourceConfig

Configuration of the resources used for the compute allocation definition.

Sourcemodule AbsoluteBorrowLimitResourceList = Awso_sagemaker.Values.AbsoluteBorrowLimitResourceList

A structure describing the source of an action.

Lists the properties of an action. An action represents an action or activity. Some examples are a workflow step and a model deployment. Generally, an action involves at least one input artifact or output artifact.

Sourcemodule ActiveClusterOperationCount = Awso_sagemaker.Values.ActiveClusterOperationCount
Sourcemodule ActiveClusterOperationName = Awso_sagemaker.Values.ActiveClusterOperationName

Creates an association between the source and the destination. A source can be associated with multiple destinations, and a destination can be associated with multiple sources. An association is a lineage tracking entity. For more information, see Amazon SageMaker ML Lineage Tracking.

Resource being access is not found.

You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

Creates an association between the source and the destination. A source can be associated with multiple destinations, and a destination can be associated with multiple sources. An association is a lineage tracking entity. For more information, see Amazon SageMaker ML Lineage Tracking.

Sourcemodule AddClusterNodeSpecificationIncrementTargetCountByInteger = Awso_sagemaker.Values.AddClusterNodeSpecificationIncrementTargetCountByInteger
Sourcemodule AddClusterNodeSpecification = Awso_sagemaker.Values.AddClusterNodeSpecification

Specifies an instance group and the number of nodes to add to it.

Sourcemodule AddClusterNodeSpecificationList = Awso_sagemaker.Values.AddClusterNodeSpecificationList

A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags. For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

Adds or overwrites one or more tags for the specified SageMaker resource. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see Amazon Web Services Tagging Strategies. Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you first create the tuning job by specifying them in the Tags parameter of CreateHyperParameterTuningJob Tags that you add to a SageMaker Domain or User Profile by calling this API are also added to any Apps that the Domain or User Profile launches after you call this API, but not to Apps that the Domain or User Profile launched before you called this API. To make sure that the tags associated with a Domain or User Profile are also added to all Apps that the Domain or User Profile launches, add the tags when you first create the Domain or User Profile by specifying them in the Tags parameter of CreateDomain or CreateUserProfile.

Adds or overwrites one or more tags for the specified SageMaker resource. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see Amazon Web Services Tagging Strategies. Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you first create the tuning job by specifying them in the Tags parameter of CreateHyperParameterTuningJob Tags that you add to a SageMaker Domain or User Profile by calling this API are also added to any Apps that the Domain or User Profile launches after you call this API, but not to Apps that the Domain or User Profile launched before you called this API. To make sure that the tags associated with a Domain or User Profile are also added to all Apps that the Domain or User Profile launches, add the tags when you first create the Domain or User Profile by specifying them in the Tags parameter of CreateDomain or CreateUserProfile.

Sourcemodule AdditionalCodeRepositoryNamesOrUrls = Awso_sagemaker.Values.AdditionalCodeRepositoryNamesOrUrls

Information about additional Elastic Network Interfaces (ENIs) associated with an instance.

Sourcemodule ProductionVariantInstanceType = Awso_sagemaker.Values.ProductionVariantInstanceType
Sourcemodule RealtimeInferenceInstanceTypes = Awso_sagemaker.Values.RealtimeInferenceInstanceTypes
Sourcemodule ModelPackageFrameworkVersion = Awso_sagemaker.Values.ModelPackageFrameworkVersion

Input object for the model.

The access configuration file to control access to the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig. If you are a Jumpstart user, see the End-user license agreements section for more details on accepting the EULA. If you are an AutoML user, see the Optional Parameters section of Create an AutoML job to fine-tune text generation models using the API for details on How to set the EULA acceptance when fine-tuning a model using the AutoML API.

Configuration information specifying which hub contents have accessible deployment options.

Specifies the S3 location of ML model data to deploy.

Specifies the location of ML model data to deploy. If specified, you must specify one and only one of the available data sources.

Identifies the foundation model that was used as the starting point for model customization.

Sourcemodule AdditionalS3DataSourceDataType = Awso_sagemaker.Values.AdditionalS3DataSourceDataType

A data source used for training or inference that is in addition to the input dataset or model data.

Sourcemodule AdditionalModelChannelName = Awso_sagemaker.Values.AdditionalModelChannelName

Data sources that are available to your model in addition to the one that you specify for ModelDataSource when you use the CreateModel action.

Sourcemodule AdditionalModelDataSources = Awso_sagemaker.Values.AdditionalModelDataSources
Sourcemodule ModelPackageContainerDefinition = Awso_sagemaker.Values.ModelPackageContainerDefinition

Describes the Docker container for the model package.

Sourcemodule ModelPackageContainerDefinitionList = Awso_sagemaker.Values.ModelPackageContainerDefinitionList
Sourcemodule AdditionalInferenceSpecificationDefinition = Awso_sagemaker.Values.AdditionalInferenceSpecificationDefinition

A structure of additional Inference Specification. Additional Inference Specification specifies details about inference jobs that can be run with models based on this model package

Sourcemodule AdditionalInferenceSpecifications = Awso_sagemaker.Values.AdditionalInferenceSpecifications

Edge Manager agent version.

Sourcemodule AggregationTransformationValue = Awso_sagemaker.Values.AggregationTransformationValue
Sourcemodule TransformationAttributeName = Awso_sagemaker.Values.TransformationAttributeName
Sourcemodule AggregationTransformations = Awso_sagemaker.Values.AggregationTransformations

An Amazon CloudWatch alarm configured to monitor metrics on an endpoint.

The details of the alarm to monitor during the AMI update.

Sourcemodule TrainingRepositoryCredentialsProviderArn = Awso_sagemaker.Values.TrainingRepositoryCredentialsProviderArn
Sourcemodule TrainingRepositoryAuthConfig = Awso_sagemaker.Values.TrainingRepositoryAuthConfig

An object containing authentication information for a private Docker registry.

Sourcemodule TrainingRepositoryAccessMode = Awso_sagemaker.Values.TrainingRepositoryAccessMode

The configuration to use an image from a private Docker registry for a training job.

Sourcemodule TrainingContainerEntrypointString = Awso_sagemaker.Values.TrainingContainerEntrypointString
Sourcemodule TrainingContainerEntrypoint = Awso_sagemaker.Values.TrainingContainerEntrypoint
Sourcemodule TrainingContainerArguments = Awso_sagemaker.Values.TrainingContainerArguments

Specifies a metric that the training algorithm writes to stderr or stdout. You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.

Specifies the training algorithm to use in a CreateTrainingJob request. SageMaker uses its own SageMaker account credentials to pull and access built-in algorithms so built-in algorithms are universally accessible across all Amazon Web Services accounts. As a result, built-in algorithms have standard, unrestricted access. You cannot restrict built-in algorithms using IAM roles. Use custom algorithms if you require specific access controls. For more information about algorithms provided by SageMaker, see Algorithms. For information about using your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.

Represents the overall status of an algorithm.

Specifies the validation and image scan statuses of the algorithm.

Provides summary information about an algorithm.

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

Describes the results of a transform job.

Describes the S3 data source.

Describes the location of the channel data.

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

Defines the input needed to run a transform job using the inference specification specified in the algorithm.

Maximum job scheduler pending time in seconds.

Specifies a limit to how long a job can run. When the job reaches the time limit, SageMaker ends the job. Use this API to cap costs. To stop a training job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost. The training algorithms provided by SageMaker automatically save the intermediate results of a model training job when possible. This attempt to save artifacts is only a best effort case as model might not be in a state from which it can be saved. For example, if training has just started, the model might not be ready to save. When saved, this intermediate data is a valid model artifact. You can use it to create a model with CreateModel. The Neural Topic Model (NTM) currently does not support saving intermediate model artifacts. When training NTMs, make sure that the maximum runtime is sufficient for the training job to complete.

Optional. Customer requested period in seconds for which the Training cluster is kept alive after the job is finished.

Specifies how instances should be placed on a specific UltraServer.

Configuration for how instances are placed and allocated within UltraServers. This is only applicable for UltraServer capacity.

Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .

Describes the resources, including machine learning (ML) compute instances and ML storage volumes, to use for model training.

Provides information about how to store model training results (model artifacts).

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

The configuration for a private hub model reference that points to a public SageMaker JumpStart model. For more information about private hubs, see Private curated hubs for foundation model access control in JumpStart.

Describes the S3 data source. Your input bucket must be in the same Amazon Web Services region as your training job.

Specifies a file system data source for a channel.

Specifies a dataset source for a channel.

Describes the location of the channel data.

A channel is a named input source that training algorithms can consume.

Defines the input needed to run a training job using the algorithm.

Sourcemodule AlgorithmValidationProfile = Awso_sagemaker.Values.AlgorithmValidationProfile

Defines a training job and a batch transform job that SageMaker runs to validate your algorithm. The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.

Sourcemodule AlgorithmValidationProfiles = Awso_sagemaker.Values.AlgorithmValidationProfiles
Sourcemodule AlgorithmValidationSpecification = Awso_sagemaker.Values.AlgorithmValidationSpecification

Specifies configurations for one or more training jobs that SageMaker runs to test the algorithm.

A collection of settings that configure the Amazon Q experience within the domain.

Sourcemodule AnnotationConsolidationConfig = Awso_sagemaker.Values.AnnotationConsolidationConfig

Configures how labels are consolidated across human workers and processes output data.

Sourcemodule StudioResourceSpecTrainingPlanArn = Awso_sagemaker.Values.StudioResourceSpecTrainingPlanArn

TrainingPlanArn variant for ResourceSpec that allows "None" to detach a training plan. Based on TrainingPlanArn (min:50, max:2048) but with min:0 and "None" in pattern.

Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on. When both SageMakerImageVersionArn and SageMakerImageArn are passed, SageMakerImageVersionArn is used. Any updates to SageMakerImageArn will not take effect if SageMakerImageVersionArn already exists in the ResourceSpec because SageMakerImageVersionArn always takes precedence. To clear the value set for SageMakerImageVersionArn, pass None as the value.

Types duplicated from IronmanApiServiceModel for federation. These types are defined in other service directories and are not available via IronmanApiServiceCommonModel.

Details about an Amazon SageMaker AI app.

The specification of a Jupyter kernel.

The Amazon Elastic File System storage configuration for a SageMaker AI image.

The configuration for the file system and kernels in a SageMaker AI image running as a KernelGateway app.

Sourcemodule CustomImageContainerEnvironmentVariables = Awso_sagemaker.Values.CustomImageContainerEnvironmentVariables
Sourcemodule CustomImageContainerEntrypoint = Awso_sagemaker.Values.CustomImageContainerEntrypoint
Sourcemodule CustomImageContainerArguments = Awso_sagemaker.Values.CustomImageContainerArguments

The configuration used to run the application image container.

The configuration for the file system and kernels in a SageMaker AI image running as a JupyterLab app. The FileSystemConfig object is not supported.

The configuration for the file system and kernels in a SageMaker image running as a Code Editor app. The FileSystemConfig object is not supported.

The configuration for running a SageMaker AI image as a KernelGateway app.

Settings related to idle shutdown of Studio applications.

Settings that are used to configure and manage the lifecycle of Amazon SageMaker Studio applications.

Sourcemodule AppSecurityGroupManagement = Awso_sagemaker.Values.AppSecurityGroupManagement

Configuration to run a processing job in a specified container image.

The ID and ID type of an artifact source.

A structure describing the source of an artifact.

Lists a summary of the properties of an artifact. An artifact represents a URI addressable object or data. Some examples are a dataset and a model.

Sourcemodule AssociateTrialComponentRequest = Awso_sagemaker.Values.AssociateTrialComponentRequest

Associates a trial component with a trial. A trial component can be associated with multiple trials. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.

Sourcemodule AssociateTrialComponentResponse = Awso_sagemaker.Values.AssociateTrialComponentResponse

Associates a trial component with a trial. A trial component can be associated with multiple trials. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.

The data type used to describe the relationship between different sources.

The IAM Identity details associated with the user. These details are associated with model package groups, model packages and project entities only.

Information about the user who created or modified a SageMaker resource.

Lists a summary of the properties of an association. An association is an entity that links other lineage or experiment entities. An example would be an association between a training job and a model.

Sourcemodule MaxConcurrentInvocationsPerInstance = Awso_sagemaker.Values.MaxConcurrentInvocationsPerInstance
Sourcemodule AsyncInferenceClientConfig = Awso_sagemaker.Values.AsyncInferenceClientConfig

Configures the behavior of the client used by SageMaker to interact with the model container during asynchronous inference.

Sourcemodule AsyncNotificationTopicTypes = Awso_sagemaker.Values.AsyncNotificationTopicTypes
Sourcemodule AsyncNotificationTopicTypeList = Awso_sagemaker.Values.AsyncNotificationTopicTypeList
Sourcemodule AsyncInferenceNotificationConfig = Awso_sagemaker.Values.AsyncInferenceNotificationConfig

Specifies the configuration for notifications of inference results for asynchronous inference.

Sourcemodule AsyncInferenceOutputConfig = Awso_sagemaker.Values.AsyncInferenceOutputConfig

Specifies the configuration for asynchronous inference invocation outputs.

Specifies configuration for how an endpoint performs asynchronous inference.

The name of the data catalog used in Athena query execution.

The name of the database used in the Athena query execution.

The name of the workgroup in which the Athena query is being started.

Sourcemodule AthenaResultCompressionType = Awso_sagemaker.Values.AthenaResultCompressionType

The SQL query statements, to be executed.

Configuration for Athena Dataset Definition input.

Sourcemodule AttachClusterNodeVolumeRequest = Awso_sagemaker.Values.AttachClusterNodeVolumeRequest

Attaches your Amazon Elastic Block Store (Amazon EBS) volume to a node in your EKS orchestrated HyperPod cluster. This API works with the Amazon Elastic Block Store (Amazon EBS) Container Storage Interface (CSI) driver to manage the lifecycle of persistent storage in your HyperPod EKS clusters.

Sourcemodule AttachClusterNodeVolumeResponse = Awso_sagemaker.Values.AttachClusterNodeVolumeResponse

Attaches your Amazon Elastic Block Store (Amazon EBS) volume to a node in your EKS orchestrated HyperPod cluster. This API works with the Amazon Elastic Block Store (Amazon EBS) Container Storage Interface (CSI) driver to manage the lifecycle of persistent storage in your HyperPod EKS clusters.

Sourcemodule AuthenticationRequestExtraParamsValue = Awso_sagemaker.Values.AuthenticationRequestExtraParamsValue
Sourcemodule AuthenticationRequestExtraParamsKey = Awso_sagemaker.Values.AuthenticationRequestExtraParamsKey
Sourcemodule AuthenticationRequestExtraParams = Awso_sagemaker.Values.AuthenticationRequestExtraParams

Contains a presigned URL and its associated local file path for downloading hub content artifacts.

The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.

Sourcemodule FinalAutoMLJobObjectiveMetric = Awso_sagemaker.Values.FinalAutoMLJobObjectiveMetric

The best candidate result from an AutoML training job.

Information about the steps for a candidate and what step it is working on.

Sourcemodule Float_ = Awso_sagemaker.Values.Float_

Information about the metric for a candidate produced by an AutoML job.

Sourcemodule CandidateArtifactLocations = Awso_sagemaker.Values.CandidateArtifactLocations

The location of artifacts for an AutoML candidate job.

The properties of an AutoML candidate job.

A list of container definitions that describe the different containers that make up an AutoML candidate. For more information, see ContainerDefinition.

Sourcemodule AutoMLContainerDefinitions = Awso_sagemaker.Values.AutoMLContainerDefinitions
Sourcemodule AutoMLInferenceContainerDefinitions = Awso_sagemaker.Values.AutoMLInferenceContainerDefinitions

Information about a candidate produced by an AutoML training job, including its status, steps, and other properties.

Sourcemodule AutoMLCandidateGenerationConfig = Awso_sagemaker.Values.AutoMLCandidateGenerationConfig

Stores the configuration information for how a candidate is generated (optional).

Describes the Amazon S3 data source.

The data source for the Autopilot job.

A channel is a named input source that training algorithms can consume. The validation dataset size is limited to less than 2 GB. The training dataset size must be less than 100 GB. For more information, see Channel. A validation dataset must contain the same headers as the training dataset.

Sourcemodule EmrServerlessComputeConfig = Awso_sagemaker.Values.EmrServerlessComputeConfig

This data type is intended for use exclusively by SageMaker Canvas and cannot be used in other contexts at the moment. Specifies the compute configuration for the EMR Serverless job.

This data type is intended for use exclusively by SageMaker Canvas and cannot be used in other contexts at the moment. Specifies the compute configuration for an AutoML job V2.

This structure specifies how to split the data into train and validation datasets. The validation and training datasets must contain the same headers. For jobs created by calling CreateAutoMLJob, the validation dataset must be less than 2 GB in size.

Sourcemodule DataExplorationNotebookLocation = Awso_sagemaker.Values.DataExplorationNotebookLocation
Sourcemodule CandidateDefinitionNotebookLocation = Awso_sagemaker.Values.CandidateDefinitionNotebookLocation

The artifacts that are generated during an AutoML job.

A channel is a named input source that training algorithms can consume. This channel is used for AutoML jobs V2 (jobs created by calling CreateAutoMLJobV2).

Sourcemodule MaxRuntimePerTrainingJobInSeconds = Awso_sagemaker.Values.MaxRuntimePerTrainingJobInSeconds
Sourcemodule MaxAutoMLJobRuntimeInSeconds = Awso_sagemaker.Values.MaxAutoMLJobRuntimeInSeconds
Sourcemodule AutoMLJobCompletionCriteria = Awso_sagemaker.Values.AutoMLJobCompletionCriteria

How long a job is allowed to run, or how many candidates a job is allowed to generate.

Security options.

A collection of settings used for an AutoML job.

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

Metadata for an AutoML job step.

Sourcemodule AutoMLPartialFailureReason = Awso_sagemaker.Values.AutoMLPartialFailureReason

The reason for a partial failure of an AutoML job.

Sourcemodule AutoMLPartialFailureReasons = Awso_sagemaker.Values.AutoMLPartialFailureReasons

Provides a summary about an AutoML job.

The output data configuration.

Sourcemodule FillingTransformationValue = Awso_sagemaker.Values.FillingTransformationValue

Transformations allowed on the dataset. Supported transformations are Filling and Aggregation. Filling specifies how to add values to missing values in the dataset. Aggregation defines how to aggregate data that does not align with forecast frequency.

Sourcemodule ItemIdentifierAttributeName = Awso_sagemaker.Values.ItemIdentifierAttributeName

The collection of components that defines the time-series.

Stores the holiday featurization attributes applicable to each item of time-series datasets during the training of a forecasting model. This allows the model to identify patterns associated with specific holidays.

Stores the configuration information for how model candidates are generated using an AutoML job V2.

Sourcemodule TimeSeriesForecastingJobConfig = Awso_sagemaker.Values.TimeSeriesForecastingJobConfig

The collection of settings used by an AutoML job V2 for the time-series forecasting problem type.

Sourcemodule TextGenerationHyperParameterValue = Awso_sagemaker.Values.TextGenerationHyperParameterValue
Sourcemodule TextGenerationHyperParameterKey = Awso_sagemaker.Values.TextGenerationHyperParameterKey
Sourcemodule TextGenerationHyperParameters = Awso_sagemaker.Values.TextGenerationHyperParameters

The collection of settings used by an AutoML job V2 for the text generation problem type. The text generation models that support fine-tuning in Autopilot are currently accessible exclusively in regions supported by Canvas. Refer to the documentation of Canvas for the full list of its supported Regions.

Sourcemodule TextClassificationJobConfig = Awso_sagemaker.Values.TextClassificationJobConfig

The collection of settings used by an AutoML job V2 for the text classification problem type.

Sourcemodule GenerateCandidateDefinitionsOnly = Awso_sagemaker.Values.GenerateCandidateDefinitionsOnly

The collection of settings used by an AutoML job V2 for the tabular problem type.

Sourcemodule ImageClassificationJobConfig = Awso_sagemaker.Values.ImageClassificationJobConfig

The collection of settings used by an AutoML job V2 for the image classification problem type.

A collection of settings specific to the problem type used to configure an AutoML job V2. There must be one and only one config of the following type.

Sourcemodule AutoMLProblemTypeConfigName = Awso_sagemaker.Values.AutoMLProblemTypeConfigName
Sourcemodule TextGenerationResolvedAttributes = Awso_sagemaker.Values.TextGenerationResolvedAttributes

The resolved attributes specific to the text generation problem type.

The resolved attributes specific to the tabular problem type.

Sourcemodule AutoMLProblemTypeResolvedAttributes = Awso_sagemaker.Values.AutoMLProblemTypeResolvedAttributes

Stores resolved attributes specific to the problem type of an AutoML job V2.

The resolved attributes used to configure an AutoML job V2.

The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you.

Automatic rollback configuration for handling endpoint deployment failures and recovery.

A flag to indicate if you want to use Autotune to automatically find optimal values for the following fields: ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize. ResourceLimits: The maximum resources that can be used for a training job. These resources include the maximum number of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time. TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for training jobs launched by a hyperparameter tuning job. RetryStrategy: The number of times to retry a training job. Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training jobs that it launches. ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model convergence.

Sourcemodule AvailabilityZoneBalanceEnforcementMode = Awso_sagemaker.Values.AvailabilityZoneBalanceEnforcementMode
Sourcemodule AvailabilityZoneBalanceMaxImbalance = Awso_sagemaker.Values.AvailabilityZoneBalanceMaxImbalance
Sourcemodule AvailableSpareInstanceCount = Awso_sagemaker.Values.AvailableSpareInstanceCount

Contains information about an available upgrade for a SageMaker Partner AI App, including the version number and release notes.

Sourcemodule AwsManagedHumanLoopRequestSource = Awso_sagemaker.Values.AwsManagedHumanLoopRequestSource
Sourcemodule BatchAddClusterNodesErrorCode = Awso_sagemaker.Values.BatchAddClusterNodesErrorCode

Information about an error that occurred during the node addition operation.

Sourcemodule BatchAddClusterNodesErrorList = Awso_sagemaker.Values.BatchAddClusterNodesErrorList
Sourcemodule BatchAddClusterNodesRequestClientTokenString = Awso_sagemaker.Values.BatchAddClusterNodesRequestClientTokenString
Sourcemodule BatchAddClusterNodesRequest = Awso_sagemaker.Values.BatchAddClusterNodesRequest

Adds nodes to a HyperPod cluster by incrementing the target count for one or more instance groups. This operation returns a unique NodeLogicalId for each node being added, which can be used to track the provisioning status of the node. This API provides a safer alternative to UpdateCluster for scaling operations by avoiding unintended configuration changes. This API is only supported for clusters using Continuous as the NodeProvisioningMode.

Information about a node that was successfully added to the cluster.

Sourcemodule BatchAddClusterNodesResponse = Awso_sagemaker.Values.BatchAddClusterNodesResponse

Adds nodes to a HyperPod cluster by incrementing the target count for one or more instance groups. This operation returns a unique NodeLogicalId for each node being added, which can be used to track the provisioning status of the node. This API provides a safer alternative to UpdateCluster for scaling operations by avoiding unintended configuration changes. This API is only supported for clusters using Continuous as the NodeProvisioningMode.

Configuration to control how SageMaker captures inference data for batch transform jobs.

Sourcemodule BatchDeleteClusterNodesErrorCode = Awso_sagemaker.Values.BatchDeleteClusterNodesErrorCode
Sourcemodule BatchDeleteClusterNodeLogicalIdsError = Awso_sagemaker.Values.BatchDeleteClusterNodeLogicalIdsError

Information about an error that occurred when attempting to delete a node identified by its NodeLogicalId.

Sourcemodule BatchDeleteClusterNodeLogicalIdsErrorList = Awso_sagemaker.Values.BatchDeleteClusterNodeLogicalIdsErrorList
Sourcemodule BatchDeleteClusterNodesError = Awso_sagemaker.Values.BatchDeleteClusterNodesError

Represents an error encountered when deleting a node from a SageMaker HyperPod cluster.

Sourcemodule BatchDeleteClusterNodesErrorList = Awso_sagemaker.Values.BatchDeleteClusterNodesErrorList
Sourcemodule BatchDeleteClusterNodesRequest = Awso_sagemaker.Values.BatchDeleteClusterNodesRequest

Deletes specific nodes within a SageMaker HyperPod cluster. BatchDeleteClusterNodes accepts a cluster name and a list of node IDs. To safeguard your work, back up your data to Amazon S3 or an FSx for Lustre file system before invoking the API on a worker node group. This will help prevent any potential data loss from the instance root volume. For more information about backup, see Use the backup script provided by SageMaker HyperPod. If you want to invoke this API on an existing cluster, you'll first need to patch the cluster by running the UpdateClusterSoftware API. For more information about patching a cluster, see Update the SageMaker HyperPod platform software of a cluster.

Sourcemodule BatchDeleteClusterNodesResponse = Awso_sagemaker.Values.BatchDeleteClusterNodesResponse

Deletes specific nodes within a SageMaker HyperPod cluster. BatchDeleteClusterNodes accepts a cluster name and a list of node IDs. To safeguard your work, back up your data to Amazon S3 or an FSx for Lustre file system before invoking the API on a worker node group. This will help prevent any potential data loss from the instance root volume. For more information about backup, see Use the backup script provided by SageMaker HyperPod. If you want to invoke this API on an existing cluster, you'll first need to patch the cluster by running the UpdateClusterSoftware API. For more information about patching a cluster, see Update the SageMaker HyperPod platform software of a cluster.

Sourcemodule BatchDescribeModelPackageError = Awso_sagemaker.Values.BatchDescribeModelPackageError

The error code and error description associated with the resource.

Sourcemodule BatchDescribeModelPackageErrorMap = Awso_sagemaker.Values.BatchDescribeModelPackageErrorMap
Sourcemodule BatchDescribeModelPackageInput = Awso_sagemaker.Values.BatchDescribeModelPackageInput

This action batch describes a list of versioned model packages

Sourcemodule ModelPackageRegistrationType = Awso_sagemaker.Values.ModelPackageRegistrationType

Defines how to perform inference generation after a training job is run.

Sourcemodule BatchDescribeModelPackageSummary = Awso_sagemaker.Values.BatchDescribeModelPackageSummary

Provides summary information about the model package.

Sourcemodule BatchDescribeModelPackageOutput = Awso_sagemaker.Values.BatchDescribeModelPackageOutput

This action batch describes a list of versioned model packages

Sourcemodule BatchRebootClusterNodesErrorCode = Awso_sagemaker.Values.BatchRebootClusterNodesErrorCode
Sourcemodule BatchRebootClusterNodeLogicalIdsError = Awso_sagemaker.Values.BatchRebootClusterNodeLogicalIdsError

Represents an error encountered when rebooting a node (identified by its logical node ID) from a SageMaker HyperPod cluster.

Sourcemodule BatchRebootClusterNodeLogicalIdsErrors = Awso_sagemaker.Values.BatchRebootClusterNodeLogicalIdsErrors
Sourcemodule BatchRebootClusterNodesError = Awso_sagemaker.Values.BatchRebootClusterNodesError

Represents an error encountered when rebooting a node from a SageMaker HyperPod cluster.

Sourcemodule BatchRebootClusterNodesErrors = Awso_sagemaker.Values.BatchRebootClusterNodesErrors
Sourcemodule BatchRebootClusterNodesRequestNodeLogicalIdsList = Awso_sagemaker.Values.BatchRebootClusterNodesRequestNodeLogicalIdsList
Sourcemodule BatchRebootClusterNodesRequestNodeIdsList = Awso_sagemaker.Values.BatchRebootClusterNodesRequestNodeIdsList
Sourcemodule BatchRebootClusterNodesRequest = Awso_sagemaker.Values.BatchRebootClusterNodesRequest

Reboots specific nodes within a SageMaker HyperPod cluster using a soft recovery mechanism. BatchRebootClusterNodes performs a graceful reboot of the specified nodes by calling the Amazon Elastic Compute Cloud RebootInstances API, which attempts to cleanly shut down the operating system before restarting the instance. This operation is useful for recovering from transient issues or applying certain configuration changes that require a restart. Rebooting a node may cause temporary service interruption for workloads running on that node. Ensure your workloads can handle node restarts or use appropriate scheduling to minimize impact. You can reboot up to 25 nodes in a single request. For SageMaker HyperPod clusters using the Slurm workload manager, ensure rebooting nodes will not disrupt critical cluster operations.

Sourcemodule BatchRebootClusterNodesResponse = Awso_sagemaker.Values.BatchRebootClusterNodesResponse

Reboots specific nodes within a SageMaker HyperPod cluster using a soft recovery mechanism. BatchRebootClusterNodes performs a graceful reboot of the specified nodes by calling the Amazon Elastic Compute Cloud RebootInstances API, which attempts to cleanly shut down the operating system before restarting the instance. This operation is useful for recovering from transient issues or applying certain configuration changes that require a restart. Rebooting a node may cause temporary service interruption for workloads running on that node. Ensure your workloads can handle node restarts or use appropriate scheduling to minimize impact. You can reboot up to 25 nodes in a single request. For SageMaker HyperPod clusters using the Slurm workload manager, ensure rebooting nodes will not disrupt critical cluster operations.

Sourcemodule BatchReplaceClusterNodesErrorCode = Awso_sagemaker.Values.BatchReplaceClusterNodesErrorCode
Sourcemodule BatchReplaceClusterNodeLogicalIdsError = Awso_sagemaker.Values.BatchReplaceClusterNodeLogicalIdsError

Represents an error encountered when replacing a node (identified by its logical node ID) in a SageMaker HyperPod cluster.

Sourcemodule BatchReplaceClusterNodeLogicalIdsErrors = Awso_sagemaker.Values.BatchReplaceClusterNodeLogicalIdsErrors
Sourcemodule BatchReplaceClusterNodesError = Awso_sagemaker.Values.BatchReplaceClusterNodesError

Represents an error encountered when replacing a node in a SageMaker HyperPod cluster.

Sourcemodule BatchReplaceClusterNodesErrors = Awso_sagemaker.Values.BatchReplaceClusterNodesErrors
Sourcemodule BatchReplaceClusterNodesRequestNodeLogicalIdsList = Awso_sagemaker.Values.BatchReplaceClusterNodesRequestNodeLogicalIdsList
Sourcemodule BatchReplaceClusterNodesRequestNodeIdsList = Awso_sagemaker.Values.BatchReplaceClusterNodesRequestNodeIdsList
Sourcemodule BatchReplaceClusterNodesRequest = Awso_sagemaker.Values.BatchReplaceClusterNodesRequest

Replaces specific nodes within a SageMaker HyperPod cluster with new hardware. BatchReplaceClusterNodes terminates the specified instances and provisions new replacement instances with the same configuration but fresh hardware. The Amazon Machine Image (AMI) and instance configuration remain the same. This operation is useful for recovering from hardware failures or persistent issues that cannot be resolved through a reboot. Data Loss Warning: Replacing nodes destroys all instance volumes, including both root and secondary volumes. All data stored on these volumes will be permanently lost and cannot be recovered. To safeguard your work, back up your data to Amazon S3 or an FSx for Lustre file system before invoking the API on a worker node group. This will help prevent any potential data loss from the instance root volume. For more information about backup, see Use the backup script provided by SageMaker HyperPod. If you want to invoke this API on an existing cluster, you'll first need to patch the cluster by running the UpdateClusterSoftware API. For more information about patching a cluster, see Update the SageMaker HyperPod platform software of a cluster. You can replace up to 25 nodes in a single request.

Sourcemodule BatchReplaceClusterNodesResponse = Awso_sagemaker.Values.BatchReplaceClusterNodesResponse

Replaces specific nodes within a SageMaker HyperPod cluster with new hardware. BatchReplaceClusterNodes terminates the specified instances and provisions new replacement instances with the same configuration but fresh hardware. The Amazon Machine Image (AMI) and instance configuration remain the same. This operation is useful for recovering from hardware failures or persistent issues that cannot be resolved through a reboot. Data Loss Warning: Replacing nodes destroys all instance volumes, including both root and secondary volumes. All data stored on these volumes will be permanently lost and cannot be recovered. To safeguard your work, back up your data to Amazon S3 or an FSx for Lustre file system before invoking the API on a worker node group. This will help prevent any potential data loss from the instance root volume. For more information about backup, see Use the backup script provided by SageMaker HyperPod. If you want to invoke this API on an existing cluster, you'll first need to patch the cluster by running the UpdateClusterSoftware API. For more information about patching a cluster, see Update the SageMaker HyperPod platform software of a cluster. You can replace up to 25 nodes in a single request.

Sourcemodule ProcessingS3DataDistributionType = Awso_sagemaker.Values.ProcessingS3DataDistributionType
Sourcemodule ProbabilityThresholdAttribute = Awso_sagemaker.Values.ProbabilityThresholdAttribute
Sourcemodule MonitoringTimeOffsetString = Awso_sagemaker.Values.MonitoringTimeOffsetString
Sourcemodule MonitoringParquetDatasetFormat = Awso_sagemaker.Values.MonitoringParquetDatasetFormat

Represents the Parquet dataset format used when running a monitoring job.

Sourcemodule MonitoringJsonDatasetFormat = Awso_sagemaker.Values.MonitoringJsonDatasetFormat

Represents the JSON dataset format used when running a monitoring job.

Sourcemodule MonitoringCsvDatasetFormat = Awso_sagemaker.Values.MonitoringCsvDatasetFormat

Represents the CSV dataset format used when running a monitoring job.

Represents the dataset format used when running a monitoring job.

Input object for the batch transform job.

Sourcemodule BedrockCustomModelDeploymentMetadata = Awso_sagemaker.Values.BedrockCustomModelDeploymentMetadata

The metadata of the Amazon Bedrock custom model deployment.

Sourcemodule BedrockCustomModelMetadata = Awso_sagemaker.Values.BedrockCustomModelMetadata

The metadata of the Amazon Bedrock custom model.

Sourcemodule BedrockModelImportMetadata = Awso_sagemaker.Values.BedrockModelImportMetadata

The metadata of the Amazon Bedrock model import.

Sourcemodule BedrockProvisionedModelThroughputMetadata = Awso_sagemaker.Values.BedrockProvisionedModelThroughputMetadata

The metadata of the Amazon Bedrock provisioned model throughput.

Sourcemodule MaxNumberOfTrainingJobsNotImproving = Awso_sagemaker.Values.MaxNumberOfTrainingJobsNotImproving

A structure that keeps track of which training jobs launched by your hyperparameter tuning job are not improving model performance as evaluated against an objective function.

Details about the metrics source.

Contains bias metrics for a model.

Specifies the type and size of the endpoint capacity to activate for a blue/green deployment, a rolling deployment, or a rollback strategy. You can specify your batches as either instance count or the overall percentage or your fleet. For a rollback strategy, if you don't specify the fields in this object, or if you set the Value to 100%, then SageMaker uses a blue/green rollback strategy and rolls all traffic back to the blue fleet.

Defines the traffic routing strategy during an endpoint deployment to shift traffic from the old fleet to the new fleet.

Sourcemodule MaximumExecutionTimeoutInSeconds = Awso_sagemaker.Values.MaximumExecutionTimeoutInSeconds

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.

Details on the cache hit of a pipeline execution step.

An output parameter of a pipeline step.

Metadata about a callback step.

The workspace settings for the SageMaker Canvas application.

Sourcemodule TimeSeriesForecastingSettings = Awso_sagemaker.Values.TimeSeriesForecastingSettings

Time series forecast settings for the SageMaker Canvas application.

The model registry settings for the SageMaker Canvas application.

The Amazon SageMaker Canvas application setting where you configure document querying.

Sourcemodule IdentityProviderOAuthSetting = Awso_sagemaker.Values.IdentityProviderOAuthSetting

The Amazon SageMaker Canvas application setting where you configure OAuth for connecting to an external data source, such as Snowflake.

Sourcemodule IdentityProviderOAuthSettings = Awso_sagemaker.Values.IdentityProviderOAuthSettings

The generative AI settings for the SageMaker Canvas application. Configure these settings for Canvas users starting chats with generative AI foundation models. For more information, see Use generative AI with foundation models.

The settings for running Amazon EMR Serverless jobs in SageMaker Canvas.

The model deployment settings for the SageMaker Canvas application. In order to enable model deployment for Canvas, the SageMaker Domain's or user profile's Amazon Web Services IAM execution role must have the AmazonSageMakerCanvasDirectDeployAccess policy attached. You can also turn on model deployment permissions through the SageMaker Domain's or user profile's settings in the SageMaker console.

The SageMaker Canvas application settings.

Information about the Capacity Reservation used by an instance or instance group.

Sourcemodule CapacityReservationPreference = Awso_sagemaker.Values.CapacityReservationPreference

The configuration of the size measurements of the AMI update. Using this configuration, you can specify whether SageMaker should update your instance group by an amount or percentage of instances.

Configuration specifying how to treat different headers. If no headers are specified Amazon SageMaker AI will by default base64 encode when capturing the data.

Specifies data Model Monitor will capture.

Sourcemodule CategoricalParameterRangeValues = Awso_sagemaker.Values.CategoricalParameterRangeValues

Environment parameters you want to benchmark your load test against.

A list of categorical hyperparameters to tune.

Sourcemodule CategoricalParameterRangeSpecification = Awso_sagemaker.Values.CategoricalParameterRangeSpecification

Defines the possible values for a categorical hyperparameter.

Sourcemodule CategoricalParameterRanges = Awso_sagemaker.Values.CategoricalParameterRanges

A key-value pair that represents a parameter for the CloudFormation stack.

The CloudFormation template provider configuration for creating infrastructure resources.

Details about the CloudFormation stack.

A key-value pair representing a parameter used in the CloudFormation stack.

A key-value pair representing a parameter used in the CloudFormation stack.

Details about a CloudFormation template provider configuration and associated provisioning information.

Contains configuration details for updating an existing CloudFormation template provider in the project.

Defines a named input source, called a channel, to be used by an algorithm.

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

The container for the metadata for the ClarifyCheck step. For more information, see the topic on ClarifyCheck step in the Amazon SageMaker Developer Guide.

A parameter used to configure the SageMaker Clarify explainer to treat text features as text so that explanations are provided for individual units of text. Required only for natural language processing (NLP) explainability.

Sourcemodule ClarifyShapNumberOfSamples = Awso_sagemaker.Values.ClarifyShapNumberOfSamples

The configuration for the SHAP baseline (also called the background or reference dataset) of the Kernal SHAP algorithm. The number of records in the baseline data determines the size of the synthetic dataset, which has an impact on latency of explainability requests. For more information, see the Synthetic data of Configure and create an endpoint. ShapBaseline and ShapBaselineUri are mutually exclusive parameters. One or the either is required to configure a SHAP baseline.

The configuration for SHAP analysis using SageMaker Clarify Explainer.

Sourcemodule ClarifyProbabilityAttribute = Awso_sagemaker.Values.ClarifyProbabilityAttribute

The inference configuration parameter for the model container.

The configuration parameters for the SageMaker Clarify explainer.

Specifies the autoscaling configuration for a HyperPod cluster.

Sourcemodule ClusterAutoScalingConfigOutput = Awso_sagemaker.Values.ClusterAutoScalingConfigOutput

The autoscaling configuration and status information for a HyperPod cluster.

Configuration options specific to Spot instances.

Configuration options specific to On-Demand instances.

Sourcemodule ClusterCapacityRequirements = Awso_sagemaker.Values.ClusterCapacityRequirements

Defines the instance capacity requirements for an instance group, including configurations for both Spot and On-Demand capacity types.

Defines the configuration for attaching an additional Amazon Elastic Block Store (EBS) volume to each instance of the SageMaker HyperPod cluster instance group. To learn more, see SageMaker HyperPod release notes: June 20, 2024.

Sourcemodule InstanceRequirementsEniConfiguration = Awso_sagemaker.Values.InstanceRequirementsEniConfiguration

The customer ENI and additional ENIs associated with a network interface category.

Sourcemodule InstanceRequirementsEniConfigurations = Awso_sagemaker.Values.InstanceRequirementsEniConfigurations

Metadata information about an instance in a HyperPod cluster.

Sourcemodule InstanceGroupScalingMetadata = Awso_sagemaker.Values.InstanceGroupScalingMetadata

Metadata information about scaling operations for an instance group.

Metadata information about an instance group in a HyperPod cluster.

Metadata information about a HyperPod cluster showing information about the cluster level operations, such as creating, updating, and deleting.

Metadata associated with a cluster event, which may include details about various resource types.

Detailed information about a specific event, including event metadata.

Detailed information about a specific event in a HyperPod cluster.

A summary of an event in a HyperPod cluster.

Defines the configuration for attaching an Amazon FSx for Lustre file system to instances in a SageMaker HyperPod cluster instance group.

Defines the configuration for attaching an Amazon FSx for OpenZFS file system to instances in a SageMaker HyperPod cluster instance group.

The configurations that SageMaker uses when updating the AMI versions.

The configuration to use when updating the AMI versions.

The configuration object of the schedule that SageMaker follows when updating the AMI.

Sourcemodule InstanceGroupTrainingPlanStatus = Awso_sagemaker.Values.InstanceGroupTrainingPlanStatus

The Slurm configuration details for an instance group in a SageMaker HyperPod cluster.

Sourcemodule ClusterNonNegativeInstanceCount = Awso_sagemaker.Values.ClusterNonNegativeInstanceCount
Sourcemodule ClusterNetworkInterfaceDetails = Awso_sagemaker.Values.ClusterNetworkInterfaceDetails

The network interface configuration details for a Amazon SageMaker HyperPod cluster instance group.

Sourcemodule ClusterLifeCycleConfigFileName = Awso_sagemaker.Values.ClusterLifeCycleConfigFileName

The lifecycle configuration for a SageMaker HyperPod cluster.

Sourcemodule ClusterKubernetesTaintValue = Awso_sagemaker.Values.ClusterKubernetesTaintValue
Sourcemodule ClusterKubernetesTaintEffect = Awso_sagemaker.Values.ClusterKubernetesTaintEffect

A Kubernetes taint that can be applied to cluster nodes.

Sourcemodule ClusterKubernetesLabelValue = Awso_sagemaker.Values.ClusterKubernetesLabelValue
Sourcemodule ClusterKubernetesConfigDetails = Awso_sagemaker.Values.ClusterKubernetesConfigDetails

Detailed Kubernetes configuration showing both the current and desired state of labels and taints for cluster nodes.

Details about a specific instance type within a flexible instance group, including the count and configuration.

Sourcemodule ClusterInstanceTypeDetails = Awso_sagemaker.Values.ClusterInstanceTypeDetails
Sourcemodule ClusterInstanceStorageConfig = Awso_sagemaker.Values.ClusterInstanceStorageConfig

Defines the configuration for attaching additional storage to the instances in the SageMaker HyperPod cluster instance group. To learn more, see SageMaker HyperPod release notes: June 20, 2024.

Sourcemodule ClusterInstanceStorageConfigs = Awso_sagemaker.Values.ClusterInstanceStorageConfigs
Sourcemodule ClusterInstanceRequirementDetails = Awso_sagemaker.Values.ClusterInstanceRequirementDetails

The instance requirement details for a flexible instance group, including the current and desired instance types.

Sourcemodule ClusterInstanceGroupDetails = Awso_sagemaker.Values.ClusterInstanceGroupDetails

Details of an instance group in a SageMaker HyperPod cluster.

Sourcemodule ClusterInstanceGroupDetailsList = Awso_sagemaker.Values.ClusterInstanceGroupDetailsList

The Slurm configuration for an instance group in a SageMaker HyperPod cluster.

The network interface configuration for a Amazon SageMaker HyperPod cluster instance group.

Kubernetes configuration that specifies labels and taints to be applied to cluster nodes in an instance group.

Sourcemodule ClusterInstanceRequirements = Awso_sagemaker.Values.ClusterInstanceRequirements

The instance requirements for a flexible instance group. Use this to specify multiple instance types that the instance group can use. The order of instance types in the list determines the priority for instance provisioning.

Sourcemodule ClusterInstanceGroupSpecification = Awso_sagemaker.Values.ClusterInstanceGroupSpecification

The specifications of an instance group that you need to define.

Sourcemodule ClusterInstanceGroupSpecifications = Awso_sagemaker.Values.ClusterInstanceGroupSpecifications
Sourcemodule ClusterInstanceGroupsToDelete = Awso_sagemaker.Values.ClusterInstanceGroupsToDelete
Sourcemodule ClusterInstanceMemoryAllocationPercentage = Awso_sagemaker.Values.ClusterInstanceMemoryAllocationPercentage

Specifies the placement details for the node in the SageMaker HyperPod cluster, including the Availability Zone and the unique identifier (ID) of the Availability Zone.

Sourcemodule ClusterInstanceStatusDetails = Awso_sagemaker.Values.ClusterInstanceStatusDetails

Details of an instance in a SageMaker HyperPod cluster.

Sourcemodule ClusterKubernetesConfigNodeDetails = Awso_sagemaker.Values.ClusterKubernetesConfigNodeDetails

Node-specific Kubernetes configuration showing both current and desired state of labels and taints for an individual cluster node.

Contains information about the UltraServer object.

Details of an instance (also called a node interchangeably) in a SageMaker HyperPod cluster.

Sourcemodule ClusterNodeProvisioningMode = Awso_sagemaker.Values.ClusterNodeProvisioningMode

Lists a summary of the properties of an instance (also called a node interchangeably) of a SageMaker HyperPod cluster.

Sourcemodule ClusterSlurmConfigStrategy = Awso_sagemaker.Values.ClusterSlurmConfigStrategy
Sourcemodule ClusterOrchestratorSlurmConfig = Awso_sagemaker.Values.ClusterOrchestratorSlurmConfig

The configuration settings for the Slurm orchestrator used with the SageMaker HyperPod cluster.

Sourcemodule ClusterOrchestratorEksConfig = Awso_sagemaker.Values.ClusterOrchestratorEksConfig

The configuration settings for the Amazon EKS cluster used as the orchestrator for the SageMaker HyperPod cluster.

The type of orchestrator used for the SageMaker HyperPod cluster.

Sourcemodule FSxLustrePerUnitStorageThroughput = Awso_sagemaker.Values.FSxLustrePerUnitStorageThroughput

Configuration settings for an Amazon FSx for Lustre file system to be used with the cluster.

The configuration details for the restricted instance groups (RIG) environment.

Sourcemodule ClusterRestrictedInstanceGroupDetails = Awso_sagemaker.Values.ClusterRestrictedInstanceGroupDetails

The instance group details of the restricted instance group (RIG).

Sourcemodule ClusterRestrictedInstanceGroupDetailsList = Awso_sagemaker.Values.ClusterRestrictedInstanceGroupDetailsList

The configuration for the restricted instance groups (RIG) environment.

Sourcemodule ClusterRestrictedInstanceGroupSpecification = Awso_sagemaker.Values.ClusterRestrictedInstanceGroupSpecification

The specifications of a restricted instance group that you need to define.

Sourcemodule ClusterRestrictedInstanceGroupSpecifications = Awso_sagemaker.Values.ClusterRestrictedInstanceGroupSpecifications
Sourcemodule ClusterSchedulerConfigSummary = Awso_sagemaker.Values.ClusterSchedulerConfigSummary

Summary of the cluster policy.

Sourcemodule ClusterSchedulerConfigSummaryList = Awso_sagemaker.Values.ClusterSchedulerConfigSummaryList
Sourcemodule ClusterSchedulerPriorityClassName = Awso_sagemaker.Values.ClusterSchedulerPriorityClassName

Lists a summary of the properties of a SageMaker HyperPod cluster.

Sourcemodule ClusterTieredStorageConfig = Awso_sagemaker.Values.ClusterTieredStorageConfig

Defines the configuration for managed tier checkpointing in a HyperPod cluster. Managed tier checkpointing uses multiple storage tiers, including cluster CPU memory, to provide faster checkpoint operations and improved fault tolerance for large-scale model training. The system automatically saves checkpoints at high frequency to memory and periodically persists them to durable storage, like Amazon S3.

A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image.

The Code Editor application settings. For more information about Code Editor, see Get started with Code Editor in Amazon SageMaker.

A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application.

Sourcemodule CodeRepositoryNameContains = Awso_sagemaker.Values.CodeRepositoryNameContains

Specifies configuration details for a Git repository in your Amazon Web Services account.

Specifies summary information about a Git repository.

Use this parameter to configure your Amazon Cognito workforce. A single Cognito workforce is created using and corresponds to a single Amazon Cognito user pool.

Identifies a Amazon Cognito user group. A user group can be used in on or more work teams.

Configuration for your vector collection type.

Configuration for your collection.

Configuration information for the Amazon SageMaker Debugger output tensor collections.

A summary of a model compilation job.

Resource sharing configuration.

Sourcemodule ComputeQuotaResourceConfigList = Awso_sagemaker.Values.ComputeQuotaResourceConfigList

Configuration of the compute allocation definition for an entity. This includes the resource sharing option and the setting to preempt low priority tasks.

Sourcemodule ComputeQuotaTargetTeamName = Awso_sagemaker.Values.ComputeQuotaTargetTeamName

The target entity to allocate compute resources to.

Summary of the compute allocation definition.

Metadata for a Condition step.

Sourcemodule ConfiguredSpareInstanceCount = Awso_sagemaker.Values.ConfiguredSpareInstanceCount

There was a conflict when you attempted to modify a SageMaker entity such as an Experiment or Artifact.

Specifies additional configuration for hosting multi-model endpoints.

Sourcemodule InferenceSpecificationName = Awso_sagemaker.Values.InferenceSpecificationName
Sourcemodule RepositoryCredentialsProviderArn = Awso_sagemaker.Values.RepositoryCredentialsProviderArn

Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field of the ImageConfig object that you passed to a call to CreateModel and the private Docker registry where the model image is hosted requires authentication.

Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC).

Describes the container, as part of model definition.

A structure describing the source of a context.

Lists a summary of the properties of a context. A context provides a logical grouping of other entities.

A list of continuous hyperparameters to tune.

Sourcemodule ContinuousParameterRangeSpecification = Awso_sagemaker.Values.ContinuousParameterRangeSpecification

Defines the possible values for a continuous hyperparameter.

A flag to indicating that automatic model tuning (AMT) has detected model convergence, defined as a lack of significant improvement (1% or less) against an objective metric.

Sourcemodule CreateAIBenchmarkJobRequest = Awso_sagemaker.Values.CreateAIBenchmarkJobRequest

Creates a benchmark job that runs performance benchmarks against inference infrastructure using a predefined AI workload configuration. The benchmark job measures metrics such as latency, throughput, and cost for your generative AI inference endpoints.

Resource being accessed is in use.

Sourcemodule CreateAIBenchmarkJobResponse = Awso_sagemaker.Values.CreateAIBenchmarkJobResponse

Creates a benchmark job that runs performance benchmarks against inference infrastructure using a predefined AI workload configuration. The benchmark job measures metrics such as latency, throughput, and cost for your generative AI inference endpoints.

Sourcemodule CreateAIRecommendationJobRequest = Awso_sagemaker.Values.CreateAIRecommendationJobRequest

Creates a recommendation job that generates intelligent optimization recommendations for generative AI inference deployments. The job analyzes your model, workload configuration, and performance targets to recommend optimal instance types, model optimization techniques (such as quantization and speculative decoding), and deployment configurations.

Sourcemodule CreateAIRecommendationJobResponse = Awso_sagemaker.Values.CreateAIRecommendationJobResponse

Creates a recommendation job that generates intelligent optimization recommendations for generative AI inference deployments. The job analyzes your model, workload configuration, and performance targets to recommend optimal instance types, model optimization techniques (such as quantization and speculative decoding), and deployment configurations.

Sourcemodule CreateAIWorkloadConfigRequest = Awso_sagemaker.Values.CreateAIWorkloadConfigRequest

Creates a reusable AI workload configuration that defines datasets, data sources, and benchmark tool settings for consistent performance testing of generative AI inference deployments on Amazon SageMaker AI.

Sourcemodule CreateAIWorkloadConfigResponse = Awso_sagemaker.Values.CreateAIWorkloadConfigResponse

Creates a reusable AI workload configuration that defines datasets, data sources, and benchmark tool settings for consistent performance testing of generative AI inference deployments on Amazon SageMaker AI.

Metadata properties of the tracking entity, trial, or trial component.

Creates an action. An action is a lineage tracking entity that represents an action or activity. For example, a model deployment or an HPO job. Generally, an action involves at least one input or output artifact. For more information, see Amazon SageMaker ML Lineage Tracking.

Creates an action. An action is a lineage tracking entity that represents an action or activity. For example, a model deployment or an HPO job. Generally, an action involves at least one input or output artifact. For more information, see Amazon SageMaker ML Lineage Tracking.

Sourcemodule HyperParameterTuningJobObjectiveType = Awso_sagemaker.Values.HyperParameterTuningJobObjectiveType
Sourcemodule HyperParameterTuningJobObjective = Awso_sagemaker.Values.HyperParameterTuningJobObjective

Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter. If you want to define a custom objective metric, see Define metrics and environment variables.

Sourcemodule HyperParameterTuningJobObjectives = Awso_sagemaker.Values.HyperParameterTuningJobObjectives
Sourcemodule IntegerParameterRangeSpecification = Awso_sagemaker.Values.IntegerParameterRangeSpecification

Defines the possible values for an integer hyperparameter.

Defines the possible values for categorical, continuous, and integer hyperparameters to be used by an algorithm.

Sourcemodule HyperParameterSpecification = Awso_sagemaker.Values.HyperParameterSpecification

Defines a hyperparameter to be used by an algorithm.

Sourcemodule HyperParameterSpecifications = Awso_sagemaker.Values.HyperParameterSpecifications

Defines how the algorithm is used for a training job.

Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.

Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.

Sourcemodule CreateAppImageConfigRequest = Awso_sagemaker.Values.CreateAppImageConfigRequest

Creates a configuration for running a SageMaker AI image as a KernelGateway app. The configuration specifies the Amazon Elastic File System storage volume on the image, and a list of the kernels in the image.

Sourcemodule CreateAppImageConfigResponse = Awso_sagemaker.Values.CreateAppImageConfigResponse

Creates a configuration for running a SageMaker AI image as a KernelGateway app. The configuration specifies the Amazon Elastic File System storage volume on the image, and a list of the kernels in the image.

Creates a running app for the specified UserProfile. This operation is automatically invoked by Amazon SageMaker AI upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.

Creates a running app for the specified UserProfile. This operation is automatically invoked by Amazon SageMaker AI upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.

Creates an artifact. An artifact is a lineage tracking entity that represents a URI addressable object or data. Some examples are the S3 URI of a dataset and the ECR registry path of an image. For more information, see Amazon SageMaker ML Lineage Tracking.

Creates an artifact. An artifact is a lineage tracking entity that represents a URI addressable object or data. Some examples are the S3 URI of a dataset and the ECR registry path of an image. For more information, see Amazon SageMaker ML Lineage Tracking.

Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

Creates an Autopilot job also referred to as Autopilot experiment or AutoML job. An AutoML job in SageMaker AI is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker AI then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AI AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment. For more information about AutoML jobs, see https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html in the SageMaker AI developer guide. We recommend using the new versions CreateAutoMLJobV2 and DescribeAutoMLJobV2, which offer backward compatibility. CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version CreateAutoMLJob, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning). Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2. You can find the best-performing model after you run an AutoML job by calling DescribeAutoMLJobV2 (recommended) or DescribeAutoMLJob.

Creates an Autopilot job also referred to as Autopilot experiment or AutoML job. An AutoML job in SageMaker AI is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker AI then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AI AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment. For more information about AutoML jobs, see https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html in the SageMaker AI developer guide. We recommend using the new versions CreateAutoMLJobV2 and DescribeAutoMLJobV2, which offer backward compatibility. CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version CreateAutoMLJob, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning). Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2. You can find the best-performing model after you run an AutoML job by calling DescribeAutoMLJobV2 (recommended) or DescribeAutoMLJob.

Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2. An AutoML job in SageMaker AI is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker AI then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AI AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment. For more information about AutoML jobs, see https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html in the SageMaker AI developer guide. AutoML jobs V2 support various problem types such as regression, binary, and multiclass classification with tabular data, text and image classification, time-series forecasting, and fine-tuning of large language models (LLMs) for text generation. CreateAutoMLJobV2 and DescribeAutoMLJobV2 are new versions of CreateAutoMLJob and DescribeAutoMLJob which offer backward compatibility. CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version CreateAutoMLJob, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning). Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2. For the list of available problem types supported by CreateAutoMLJobV2, see AutoMLProblemTypeConfig. You can find the best-performing model after you run an AutoML job V2 by calling DescribeAutoMLJobV2.

Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2. An AutoML job in SageMaker AI is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker AI then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AI AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment. For more information about AutoML jobs, see https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html in the SageMaker AI developer guide. AutoML jobs V2 support various problem types such as regression, binary, and multiclass classification with tabular data, text and image classification, time-series forecasting, and fine-tuning of large language models (LLMs) for text generation. CreateAutoMLJobV2 and DescribeAutoMLJobV2 are new versions of CreateAutoMLJob and DescribeAutoMLJob which offer backward compatibility. CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version CreateAutoMLJob, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning). Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2. For the list of available problem types supported by CreateAutoMLJobV2, see AutoMLProblemTypeConfig. You can find the best-performing model after you run an AutoML job V2 by calling DescribeAutoMLJobV2.

Creates an Amazon SageMaker HyperPod cluster. SageMaker HyperPod is a capability of SageMaker for creating and managing persistent clusters for developing large machine learning models, such as large language models (LLMs) and diffusion models. To learn more, see Amazon SageMaker HyperPod in the Amazon SageMaker Developer Guide.

Creates an Amazon SageMaker HyperPod cluster. SageMaker HyperPod is a capability of SageMaker for creating and managing persistent clusters for developing large machine learning models, such as large language models (LLMs) and diffusion models. To learn more, see Amazon SageMaker HyperPod in the Amazon SageMaker Developer Guide.

Priority class configuration. When included in PriorityClasses, these class configurations define how tasks are queued.

Cluster policy configuration. This policy is used for task prioritization and fair-share allocation. This helps prioritize critical workloads and distributes idle compute across entities.

Sourcemodule CreateClusterSchedulerConfigRequest = Awso_sagemaker.Values.CreateClusterSchedulerConfigRequest

Create cluster policy configuration. This policy is used for task prioritization and fair-share allocation of idle compute. This helps prioritize critical workloads and distributes idle compute across entities.

Sourcemodule CreateClusterSchedulerConfigResponse = Awso_sagemaker.Values.CreateClusterSchedulerConfigResponse

Create cluster policy configuration. This policy is used for task prioritization and fair-share allocation of idle compute. This helps prioritize critical workloads and distributes idle compute across entities.

Creates a Git repository as a resource in your SageMaker AI account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your SageMaker AI account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with. The repository can be hosted either in Amazon Web Services CodeCommit or in any other Git repository.

Sourcemodule CreateCodeRepositoryOutput = Awso_sagemaker.Values.CreateCodeRepositoryOutput

Creates a Git repository as a resource in your SageMaker AI account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your SageMaker AI account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with. The repository can be hosted either in Amazon Web Services CodeCommit or in any other Git repository.

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.

Contains information about the output location for the compiled model and the target device that the model runs on. TargetDevice and TargetPlatform are mutually exclusive, so you need to choose one between the two to specify your target device or platform. If you cannot find your device you want to use from the TargetDevice list, use TargetPlatform to describe the platform of your edge device and CompilerOptions if there are specific settings that are required or recommended to use for particular TargetPlatform.

The VpcConfig configuration object that specifies the VPC that you want the compilation jobs to connect to. For more information on controlling access to your Amazon S3 buckets used for compilation job, see Give Amazon SageMaker AI Compilation Jobs Access to Resources in Your Amazon VPC.

Contains information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.

Sourcemodule CreateCompilationJobRequest = Awso_sagemaker.Values.CreateCompilationJobRequest

Starts a model compilation job. After the model has been compiled, Amazon SageMaker AI saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify. If you choose to host your model using Amazon SageMaker AI hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with Amazon Web Services IoT Greengrass. In that case, deploy them as an ML resource. In the request body, you provide the following: A name for the compilation job Information about the input model artifacts The output location for the compiled model and the device (target) that the model runs on The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker AI assumes to perform the model compilation job. You can also provide a Tag to track the model compilation job's resource use and costs. The response body contains the CompilationJobArn for the compiled job. To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.

Sourcemodule CreateCompilationJobResponse = Awso_sagemaker.Values.CreateCompilationJobResponse

Starts a model compilation job. After the model has been compiled, Amazon SageMaker AI saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify. If you choose to host your model using Amazon SageMaker AI hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with Amazon Web Services IoT Greengrass. In that case, deploy them as an ML resource. In the request body, you provide the following: A name for the compilation job Information about the input model artifacts The output location for the compiled model and the device (target) that the model runs on The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker AI assumes to perform the model compilation job. You can also provide a Tag to track the model compilation job's resource use and costs. The response body contains the CompilationJobArn for the compiled job. To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.

Create compute allocation definition. This defines how compute is allocated, shared, and borrowed for specified entities. Specifically, how to lend and borrow idle compute and assign a fair-share weight to the specified entities.

Sourcemodule CreateComputeQuotaResponse = Awso_sagemaker.Values.CreateComputeQuotaResponse

Create compute allocation definition. This defines how compute is allocated, shared, and borrowed for specified entities. Specifically, how to lend and borrow idle compute and assign a fair-share weight to the specified entities.

Creates a context. A context is a lineage tracking entity that represents a logical grouping of other tracking or experiment entities. Some examples are an endpoint and a model package. For more information, see Amazon SageMaker ML Lineage Tracking.

Creates a context. A context is a lineage tracking entity that represents a logical grouping of other tracking or experiment entities. Some examples are an endpoint and a model package. For more information, see Amazon SageMaker ML Lineage Tracking.

Sourcemodule MonitoringMaxRuntimeInSeconds = Awso_sagemaker.Values.MonitoringMaxRuntimeInSeconds
Sourcemodule MonitoringStoppingCondition = Awso_sagemaker.Values.MonitoringStoppingCondition

A time limit for how long the monitoring job is allowed to run before stopping.

Configuration for the cluster used to run model monitoring jobs.

Identifies the resources to deploy for a monitoring job.

Information about where and how you want to store the results of a monitoring job.

The output object for a monitoring job.

The output configuration for monitoring jobs.

The networking configuration for the monitoring job.

Sourcemodule MonitoringJobDefinitionName = Awso_sagemaker.Values.MonitoringJobDefinitionName

Input object for the endpoint

The input for the data quality monitoring job. Currently endpoints are supported for input.

Sourcemodule MonitoringStatisticsResource = Awso_sagemaker.Values.MonitoringStatisticsResource

The statistics resource for a monitoring job.

Sourcemodule MonitoringConstraintsResource = Awso_sagemaker.Values.MonitoringConstraintsResource

The constraints resource for a monitoring job.

Configuration for monitoring constraints and monitoring statistics. These baseline resources are compared against the results of the current job from the series of jobs scheduled to collect data periodically.

Sourcemodule ProcessingEnvironmentValue = Awso_sagemaker.Values.ProcessingEnvironmentValue
Sourcemodule MonitoringContainerArguments = Awso_sagemaker.Values.MonitoringContainerArguments
Sourcemodule DataQualityAppSpecification = Awso_sagemaker.Values.DataQualityAppSpecification

Information about the container that a data quality monitoring job runs.

Sourcemodule CreateDataQualityJobDefinitionRequest = Awso_sagemaker.Values.CreateDataQualityJobDefinitionRequest

Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker AI Model Monitor.

Sourcemodule MonitoringJobDefinitionArn = Awso_sagemaker.Values.MonitoringJobDefinitionArn
Sourcemodule CreateDataQualityJobDefinitionResponse = Awso_sagemaker.Values.CreateDataQualityJobDefinitionResponse

Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker AI Model Monitor.

The output configuration.

Creates a device fleet.

The TensorBoard app settings.

The SageMaker images that are hidden from the Studio user interface. You must specify the SageMaker image name and version aliases.

Sourcemodule HiddenSageMakerImageVersionAliasesList = Awso_sagemaker.Values.HiddenSageMakerImageVersionAliasesList
Sourcemodule ExecutionRoleSessionNameMode = Awso_sagemaker.Values.ExecutionRoleSessionNameMode

Studio settings. If these settings are applied on a user level, they take priority over the settings applied on a domain level.

Specifies options for sharing Amazon SageMaker AI Studio notebooks. These settings are specified as part of DefaultUserSettings when the CreateDomain API is called, and as part of UserSettings when the CreateUserProfile API is called. When SharingSettings is not specified, notebook sharing isn't allowed.

Sourcemodule RStudioServerProAccessStatus = Awso_sagemaker.Values.RStudioServerProAccessStatus
Sourcemodule RStudioServerProAppSettings = Awso_sagemaker.Values.RStudioServerProAppSettings

A collection of settings that configure user interaction with the RStudioServerPro app.

A collection of settings that apply to an RSessionGateway app.

The KernelGateway app settings.

The JupyterServer app settings.

The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.

The settings for the JupyterLab application.

A collection of default EBS storage settings that apply to spaces created within a domain or user profile.

Sourcemodule DefaultSpaceStorageSettings = Awso_sagemaker.Values.DefaultSpaceStorageSettings

The default storage settings for a space.

Details about the POSIX identity that is used for file system operations.

Configuration for the custom Amazon S3 file system.

The settings for assigning a custom Amazon FSx for Lustre file system to a user profile or space for an Amazon SageMaker Domain.

The settings for assigning a custom Amazon EFS file system to a user profile or space for an Amazon SageMaker AI Domain.

The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.

A collection of settings that apply to users in a domain. These settings are specified when the CreateUserProfile API is called, and as DefaultUserSettings when the CreateDomain API is called. SecurityGroups is aggregated when specified in both calls. For all other settings in UserSettings, the values specified in CreateUserProfile take precedence over those specified in CreateDomain.

Sourcemodule UnifiedStudioEnvironmentId = Awso_sagemaker.Values.UnifiedStudioEnvironmentId
Sourcemodule SingleSignOnApplicationArn = Awso_sagemaker.Values.SingleSignOnApplicationArn

The settings that apply to an Amazon SageMaker AI domain when you use it in Amazon SageMaker Unified Studio.

Sourcemodule TrustedIdentityPropagationSettings = Awso_sagemaker.Values.TrustedIdentityPropagationSettings

The Trusted Identity Propagation (TIP) settings for the SageMaker domain. These settings determine how user identities from IAM Identity Center are propagated through the domain to TIP enabled Amazon Web Services services.

Sourcemodule RStudioServerProDomainSettings = Awso_sagemaker.Values.RStudioServerProDomainSettings

A collection of settings that configure the RStudioServerPro Domain-level app.

Sourcemodule ExecutionRoleIdentityConfig = Awso_sagemaker.Values.ExecutionRoleIdentityConfig

A collection of settings that configure the domain's Docker interaction.

A collection of settings that apply to the SageMaker Domain. These settings are specified through the CreateDomain API call.

The default settings for shared spaces that users create in the domain. SageMaker applies these settings only to shared spaces. It doesn't apply them to private spaces.

Creates a Domain. A domain consists of an associated Amazon Elastic File System volume, a list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. Users within a domain can share notebook files and other artifacts with each other. EFS storage When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files. SageMaker AI uses the Amazon Web Services Key Management Service (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, you can specify a customer managed key. For more information, see Protect Data at Rest Using Encryption. VPC configuration All traffic between the domain and the Amazon EFS volume is through the specified VPC and subnets. For other traffic, you can specify the AppNetworkAccessType parameter. AppNetworkAccessType corresponds to the network access type that you choose when you onboard to the domain. The following options are available: PublicInternetOnly - Non-EFS traffic goes through a VPC managed by Amazon SageMaker AI, which allows internet access. This is the default value. VpcOnly - All traffic is through the specified VPC and subnets. Internet access is disabled by default. To allow internet access, you must specify a NAT gateway. When internet access is disabled, you won't be able to run a Amazon SageMaker AI Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker AI API and runtime or a NAT gateway and your security groups allow outbound connections. NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a Amazon SageMaker AI Studio app successfully. For more information, see Connect Amazon SageMaker AI Studio Notebooks to Resources in a VPC.

Creates a Domain. A domain consists of an associated Amazon Elastic File System volume, a list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. Users within a domain can share notebook files and other artifacts with each other. EFS storage When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files. SageMaker AI uses the Amazon Web Services Key Management Service (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, you can specify a customer managed key. For more information, see Protect Data at Rest Using Encryption. VPC configuration All traffic between the domain and the Amazon EFS volume is through the specified VPC and subnets. For other traffic, you can specify the AppNetworkAccessType parameter. AppNetworkAccessType corresponds to the network access type that you choose when you onboard to the domain. The following options are available: PublicInternetOnly - Non-EFS traffic goes through a VPC managed by Amazon SageMaker AI, which allows internet access. This is the default value. VpcOnly - All traffic is through the specified VPC and subnets. Internet access is disabled by default. To allow internet access, you must specify a NAT gateway. When internet access is disabled, you won't be able to run a Amazon SageMaker AI Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker AI API and runtime or a NAT gateway and your security groups allow outbound connections. NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a Amazon SageMaker AI Studio app successfully. For more information, see Connect Amazon SageMaker AI Studio Notebooks to Resources in a VPC.

Contains information about the configuration of a model in a deployment.

Sourcemodule EdgeDeploymentModelConfigs = Awso_sagemaker.Values.EdgeDeploymentModelConfigs

Contains information about the configuration of a deployment.

Contains information about the configurations of selected devices.

Contains information about a stage in an edge deployment plan.

Sourcemodule CreateEdgeDeploymentPlanRequest = Awso_sagemaker.Values.CreateEdgeDeploymentPlanRequest

Creates an edge deployment plan, consisting of multiple stages. Each stage may have a different deployment configuration and devices.

Sourcemodule CreateEdgeDeploymentPlanResponse = Awso_sagemaker.Values.CreateEdgeDeploymentPlanResponse

Creates an edge deployment plan, consisting of multiple stages. Each stage may have a different deployment configuration and devices.

Sourcemodule CreateEdgeDeploymentStageRequest = Awso_sagemaker.Values.CreateEdgeDeploymentStageRequest

Creates a new stage in an existing edge deployment plan.

Sourcemodule CreateEdgePackagingJobRequest = Awso_sagemaker.Values.CreateEdgePackagingJobRequest

Starts a SageMaker Edge Manager model packaging job. Edge Manager will use the model artifacts from the Amazon Simple Storage Service bucket that you specify. After the model has been packaged, Amazon SageMaker saves the resulting artifacts to an S3 bucket that you specify.

Sourcemodule VariantInstanceProvisionTimeoutInSeconds = Awso_sagemaker.Values.VariantInstanceProvisionTimeoutInSeconds
Sourcemodule ProductionVariantVolumeSizeInGB = Awso_sagemaker.Values.ProductionVariantVolumeSizeInGB
Sourcemodule ServerlessProvisionedConcurrency = Awso_sagemaker.Values.ServerlessProvisionedConcurrency
Sourcemodule ProductionVariantServerlessConfig = Awso_sagemaker.Values.ProductionVariantServerlessConfig

Specifies the serverless configuration for an endpoint variant.

Sourcemodule ProductionVariantSSMAccess = Awso_sagemaker.Values.ProductionVariantSSMAccess
Sourcemodule ProductionVariantRoutingConfig = Awso_sagemaker.Values.ProductionVariantRoutingConfig

Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.

Sourcemodule ProductionVariantModelDataDownloadTimeoutInSeconds = Awso_sagemaker.Values.ProductionVariantModelDataDownloadTimeoutInSeconds
Sourcemodule ManagedInstanceScalingScaleInStrategy = Awso_sagemaker.Values.ManagedInstanceScalingScaleInStrategy
Sourcemodule ManagedInstanceScalingMaximumStepSize = Awso_sagemaker.Values.ManagedInstanceScalingMaximumStepSize
Sourcemodule ManagedInstanceScalingCooldownInMinutes = Awso_sagemaker.Values.ManagedInstanceScalingCooldownInMinutes
Sourcemodule ProductionVariantManagedInstanceScalingScaleInPolicy = Awso_sagemaker.Values.ProductionVariantManagedInstanceScalingScaleInPolicy

Configures the scale-in behavior for managed instance scaling.

Sourcemodule ManagedInstanceScalingStatus = Awso_sagemaker.Values.ManagedInstanceScalingStatus
Sourcemodule ManagedInstanceScalingMinInstanceCount = Awso_sagemaker.Values.ManagedInstanceScalingMinInstanceCount
Sourcemodule ManagedInstanceScalingMaxInstanceCount = Awso_sagemaker.Values.ManagedInstanceScalingMaxInstanceCount
Sourcemodule ProductionVariantManagedInstanceScaling = Awso_sagemaker.Values.ProductionVariantManagedInstanceScaling

Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.

Sourcemodule ProductionVariantInferenceAmiVersion = Awso_sagemaker.Values.ProductionVariantInferenceAmiVersion
Sourcemodule ProductionVariantCoreDumpConfig = Awso_sagemaker.Values.ProductionVariantCoreDumpConfig

Specifies configuration for a core dump from the model container when the process crashes.

Sourcemodule ProductionVariantContainerStartupHealthCheckTimeoutInSeconds = Awso_sagemaker.Values.ProductionVariantContainerStartupHealthCheckTimeoutInSeconds
Sourcemodule ProductionVariantCapacityReservationConfig = Awso_sagemaker.Values.ProductionVariantCapacityReservationConfig

Settings for the capacity reservation for the compute instances that SageMaker AI reserves for an endpoint.

Sourcemodule ProductionVariantAcceleratorType = Awso_sagemaker.Values.ProductionVariantAcceleratorType

Specifies an instance type and its priority for a heterogeneous endpoint. Use instance pools to configure a production variant with multiple instance types, enabling the endpoint to provision instances across different types based on priority.

Identifies a model that you want to host and the resources chosen to deploy for hosting it. If you are deploying multiple models, tell SageMaker how to distribute traffic among the models by specifying variant weights. For more information on production variants, check Production variants.

Sourcemodule MetricPublishFrequencyInSeconds = Awso_sagemaker.Values.MetricPublishFrequencyInSeconds

The configuration for Utilization metrics.

A parameter to activate explainers.

Configuration to control how SageMaker AI captures inference data.

Creates an endpoint configuration that SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the CreateModel API, to deploy and the resources that you want SageMaker to provision. Then you call the CreateEndpoint API. Use this API if you want to use SageMaker hosting services to deploy models into production. In the request, you define a ProductionVariant, for each model that you want to deploy. Each ProductionVariant parameter also describes the resources that you want SageMaker to provision. This includes the number and type of ML compute instances to deploy. If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B. When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting Eventually Consistent Reads , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.

Sourcemodule CreateEndpointConfigOutput = Awso_sagemaker.Values.CreateEndpointConfigOutput

Creates an endpoint configuration that SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the CreateModel API, to deploy and the resources that you want SageMaker to provision. Then you call the CreateEndpoint API. Use this API if you want to use SageMaker hosting services to deploy models into production. In the request, you define a ProductionVariant, for each model that you want to deploy. Each ProductionVariant parameter also describes the resources that you want SageMaker to provision. This includes the number and type of ML compute instances to deploy. If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B. When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting Eventually Consistent Reads , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.

Specifies a rolling deployment strategy for updating a SageMaker endpoint.

The deployment configuration for an endpoint, which contains the desired deployment strategy and rollback configurations.

Creates an endpoint using the endpoint configuration specified in the request. SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API. Use this API to deploy models using SageMaker hosting services. You must not delete an EndpointConfig that is in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig. The endpoint name must be unique within an Amazon Web Services Region in your Amazon Web Services account. When it receives the request, SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them. When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting Eventually Consistent Reads , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read. When SageMaker receives the request, it sets the endpoint status to Creating. After it creates the endpoint, it sets the status to InService. SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API. If any of the models hosted at this endpoint get model data from an Amazon S3 location, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide. To add the IAM role policies for using this API operation, go to the IAM console, and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the role. Option 1: For a full SageMaker access, search and attach the AmazonSageMakerFullAccess policy. Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the JSON file of the IAM role: "Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"] "Resource": [ "arn:aws:sagemaker:region:account-id:endpoint/endpointName" "arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName" ] For more information, see SageMaker API Permissions: Actions, Permissions, and Resources Reference.

Creates an endpoint using the endpoint configuration specified in the request. SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API. Use this API to deploy models using SageMaker hosting services. You must not delete an EndpointConfig that is in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig. The endpoint name must be unique within an Amazon Web Services Region in your Amazon Web Services account. When it receives the request, SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them. When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting Eventually Consistent Reads , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read. When SageMaker receives the request, it sets the endpoint status to Creating. After it creates the endpoint, it sets the status to InService. SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API. If any of the models hosted at this endpoint get model data from an Amazon S3 location, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide. To add the IAM role policies for using this API operation, go to the IAM console, and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the role. Option 1: For a full SageMaker access, search and attach the AmazonSageMakerFullAccess policy. Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the JSON file of the IAM role: "Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"] "Resource": [ "arn:aws:sagemaker:region:account-id:endpoint/endpointName" "arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName" ] For more information, see SageMaker API Permissions: Actions, Permissions, and Resources Reference.

Creates a SageMaker experiment. An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called trial components, that produce a machine learning model. In the Studio UI, trials are referred to as run groups and trial components are referred to as runs. The goal of an experiment is to determine the components that produce the best model. Multiple trials are performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the remaining inputs constant. When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK. You can add tags to experiments, trials, trial components and then use the Search API to search for the tags. To add a description to an experiment, specify the optional Description parameter. To add a description later, or to change the description, call the UpdateExperiment API. To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To create a trial call the CreateTrial API.

Creates a SageMaker experiment. An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called trial components, that produce a machine learning model. In the Studio UI, trials are referred to as run groups and trial components are referred to as runs. The goal of an experiment is to determine the components that produce the best model. Multiple trials are performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the remaining inputs constant. When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK. You can add tags to experiments, trials, trial components and then use the Search API to search for the tags. To add a description to an experiment, specify the optional Description parameter. To add a description later, or to change the description, call the UpdateExperiment API. To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To create a trial call the CreateTrial API.

Used to set feature group throughput configuration. There are two modes: ON_DEMAND and PROVISIONED. With on-demand mode, you are charged for data reads and writes that your application performs on your feature group. You do not need to specify read and write throughput because Feature Store accommodates your workloads as they ramp up and down. You can switch a feature group to on-demand only once in a 24 hour period. With provisioned throughput mode, you specify the read and write capacity per second that you expect your application to require, and you are billed based on those limits. Exceeding provisioned throughput will result in your requests being throttled. Note: PROVISIONED throughput mode is supported only for feature groups that are offline-only, or use the Standard tier online store.

Time to live duration, where the record is hard deleted after the expiration time is reached; ExpiresAt = EventTime + TtlDuration. For information on HardDelete, see the DeleteRecord API in the Amazon SageMaker API Reference guide.

The security configuration for OnlineStore.

Use this to specify the Amazon Web Services Key Management Service (KMS) Key ID, or KMSKeyId, for at rest data encryption. You can turn OnlineStore on or off by specifying the EnableOnlineStore flag at General Assembly. The default value is False.

The Amazon Simple Storage (Amazon S3) location and security configuration for OfflineStore.

The meta data of the Glue table which serves as data catalog for the OfflineStore.

The configuration of an OfflineStore. Provide an OfflineStoreConfig in a request to CreateFeatureGroup to create an OfflineStore. To encrypt an OfflineStore using at rest data encryption, specify Amazon Web Services Key Management Service (KMS) key ID, or KMSKeyId, in S3StorageConfig.

A list of features. You must include FeatureName and FeatureType. Valid feature FeatureTypes are Integral, Fractional and String.

Create a new FeatureGroup. A FeatureGroup is a group of Features defined in the FeatureStore to describe a Record. The FeatureGroup defines the schema and features contained in the FeatureGroup. A FeatureGroup definition is composed of a list of Features, a RecordIdentifierFeatureName, an EventTimeFeatureName and configurations for its OnlineStore and OfflineStore. Check Amazon Web Services service quotas to see the FeatureGroups quota for your Amazon Web Services account. Note that it can take approximately 10-15 minutes to provision an OnlineStore FeatureGroup with the InMemory StorageType. You must include at least one of OnlineStoreConfig and OfflineStoreConfig to create a FeatureGroup.

Sourcemodule CreateFeatureGroupResponse = Awso_sagemaker.Values.CreateFeatureGroupResponse

Create a new FeatureGroup. A FeatureGroup is a group of Features defined in the FeatureStore to describe a Record. The FeatureGroup defines the schema and features contained in the FeatureGroup. A FeatureGroup definition is composed of a list of Features, a RecordIdentifierFeatureName, an EventTimeFeatureName and configurations for its OnlineStore and OfflineStore. Check Amazon Web Services service quotas to see the FeatureGroups quota for your Amazon Web Services account. Note that it can take approximately 10-15 minutes to provision an OnlineStore FeatureGroup with the InMemory StorageType. You must include at least one of OnlineStoreConfig and OfflineStoreConfig to create a FeatureGroup.

Container for configuring the source of human task requests.

Represents an amount of money in United States dollars.

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

Sourcemodule FlowDefinitionTaskTimeLimitInSeconds = Awso_sagemaker.Values.FlowDefinitionTaskTimeLimitInSeconds
Sourcemodule FlowDefinitionTaskKeywords = Awso_sagemaker.Values.FlowDefinitionTaskKeywords
Sourcemodule FlowDefinitionTaskDescription = Awso_sagemaker.Values.FlowDefinitionTaskDescription
Sourcemodule FlowDefinitionTaskAvailabilityLifetimeInSeconds = Awso_sagemaker.Values.FlowDefinitionTaskAvailabilityLifetimeInSeconds

Describes the work to be performed by human workers.

Sourcemodule HumanLoopActivationConditions = Awso_sagemaker.Values.HumanLoopActivationConditions
Sourcemodule HumanLoopActivationConditionsConfig = Awso_sagemaker.Values.HumanLoopActivationConditionsConfig

Defines under what conditions SageMaker creates a human loop. Used within CreateFlowDefinition. See HumanLoopActivationConditionsConfig for the required format of activation conditions.

Provides information about how and under what conditions SageMaker creates a human loop. If HumanLoopActivationConfig is not given, then all requests go to humans.

Sourcemodule FlowDefinitionOutputConfig = Awso_sagemaker.Values.FlowDefinitionOutputConfig

Contains information about where human output will be stored.

Sourcemodule CreateFlowDefinitionRequest = Awso_sagemaker.Values.CreateFlowDefinitionRequest

Creates a flow definition.

Sourcemodule CreateFlowDefinitionResponse = Awso_sagemaker.Values.CreateFlowDefinitionResponse

Creates a flow definition.

Configuration for accessing hub content through presigned URLs, including license agreement acceptance and URL validation settings.

Sourcemodule CreateHubContentPresignedUrlsRequest = Awso_sagemaker.Values.CreateHubContentPresignedUrlsRequest

Creates presigned URLs for accessing hub content artifacts. This operation generates time-limited, secure URLs that allow direct download of model artifacts and associated files from Amazon SageMaker hub content, including gated models that require end-user license agreement acceptance.

Sourcemodule CreateHubContentPresignedUrlsResponse = Awso_sagemaker.Values.CreateHubContentPresignedUrlsResponse

Creates presigned URLs for accessing hub content artifacts. This operation generates time-limited, secure URLs that allow direct download of model artifacts and associated files from Amazon SageMaker hub content, including gated models that require end-user license agreement acceptance.

Sourcemodule SageMakerPublicHubContentArn = Awso_sagemaker.Values.SageMakerPublicHubContentArn
Sourcemodule CreateHubContentReferenceRequest = Awso_sagemaker.Values.CreateHubContentReferenceRequest

Create a hub content reference in order to add a model in the JumpStart public hub to a private hub.

Sourcemodule CreateHubContentReferenceResponse = Awso_sagemaker.Values.CreateHubContentReferenceResponse

Create a hub content reference in order to add a model in the JumpStart public hub to a private hub.

The Amazon S3 storage configuration of a hub.

Create a hub.

Create a hub.

The Liquid template for the worker user interface.

Defines the settings you will use for the human review workflow user interface. Reviewers will see a three-panel interface with an instruction area, the item to review, and an input area.

Defines the settings you will use for the human review workflow user interface. Reviewers will see a three-panel interface with an instruction area, the item to review, and an input area.

Sourcemodule HyperParameterTuningJobName = Awso_sagemaker.Values.HyperParameterTuningJobName
Sourcemodule ParentHyperParameterTuningJob = Awso_sagemaker.Values.ParentHyperParameterTuningJob

A previously completed or stopped hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.

Sourcemodule ParentHyperParameterTuningJobs = Awso_sagemaker.Values.ParentHyperParameterTuningJobs
Sourcemodule HyperParameterTuningJobWarmStartType = Awso_sagemaker.Values.HyperParameterTuningJobWarmStartType
Sourcemodule HyperParameterTuningJobWarmStartConfig = Awso_sagemaker.Values.HyperParameterTuningJobWarmStartConfig

Specifies the configuration for a hyperparameter tuning job that uses one or more previous hyperparameter tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job. All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric, and the training job that performs the best is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job. All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.

Sourcemodule TargetObjectiveMetricValue = Awso_sagemaker.Values.TargetObjectiveMetricValue
Sourcemodule TuningJobCompletionCriteria = Awso_sagemaker.Values.TuningJobCompletionCriteria

The job completion criteria.

Sourcemodule TrainingJobEarlyStoppingType = Awso_sagemaker.Values.TrainingJobEarlyStoppingType
Sourcemodule HyperParameterTuningMaxRuntimeInSeconds = Awso_sagemaker.Values.HyperParameterTuningMaxRuntimeInSeconds

Specifies the maximum number of training jobs and parallel training jobs that a hyperparameter tuning job can launch.

For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.

Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job. The maximum number of items specified for Array Members refers to the maximum number of hyperparameters for each range and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of hyperparameters for all the ranges can't exceed the maximum number specified.

Sourcemodule HyperParameterTuningJobStrategyType = Awso_sagemaker.Values.HyperParameterTuningJobStrategyType
Sourcemodule HyperbandStrategyMinResource = Awso_sagemaker.Values.HyperbandStrategyMinResource
Sourcemodule HyperbandStrategyMaxResource = Awso_sagemaker.Values.HyperbandStrategyMaxResource

The configuration for Hyperband, a multi-fidelity based hyperparameter tuning strategy. Hyperband uses the final and intermediate results of a training job to dynamically allocate resources to utilized hyperparameter configurations while automatically stopping under-performing configurations. This parameter should be provided only if Hyperband is selected as the StrategyConfig under the HyperParameterTuningJobConfig API.

Sourcemodule HyperParameterTuningJobStrategyConfig = Awso_sagemaker.Values.HyperParameterTuningJobStrategyConfig

The configuration for a training job launched by a hyperparameter tuning job. Choose Bayesian for Bayesian optimization, and Random for random search optimization. For more advanced use cases, use Hyperband, which evaluates objective metrics for training jobs after every epoch. For more information about strategies, see How Hyperparameter Tuning Works.

Sourcemodule HyperParameterTuningJobConfig = Awso_sagemaker.Values.HyperParameterTuningJobConfig

Configures a hyperparameter tuning job.

The retry strategy to use when a training job fails due to an InternalServerError. RetryStrategy is specified as part of the CreateTrainingJob and CreateHyperParameterTuningJob requests. You can add the StoppingCondition parameter to the request to limit the training time for the complete job.

Sourcemodule HyperParameterTuningInstanceConfig = Awso_sagemaker.Values.HyperParameterTuningInstanceConfig

The configuration for hyperparameter tuning resources for use in training jobs launched by the tuning job. These resources include compute instances and storage volumes. Specify one or more compute instance configurations and allocation strategies to select resources (optional).

Sourcemodule HyperParameterTuningInstanceConfigs = Awso_sagemaker.Values.HyperParameterTuningInstanceConfigs
Sourcemodule HyperParameterTuningAllocationStrategy = Awso_sagemaker.Values.HyperParameterTuningAllocationStrategy
Sourcemodule HyperParameterTuningResourceConfig = Awso_sagemaker.Values.HyperParameterTuningResourceConfig

The configuration of resources, including compute instances and storage volumes for use in training jobs launched by hyperparameter tuning jobs. HyperParameterTuningResourceConfig is similar to ResourceConfig, but has the additional InstanceConfigs and AllocationStrategy fields to allow for flexible instance management. Specify one or more instance types, count, and the allocation strategy for instance selection. HyperParameterTuningResourceConfig supports the capabilities of ResourceConfig with the exception of KeepAlivePeriodInSeconds. Hyperparameter tuning jobs use warm pools by default, which reuse clusters between training jobs.

Sourcemodule HyperParameterTrainingJobEnvironmentValue = Awso_sagemaker.Values.HyperParameterTrainingJobEnvironmentValue
Sourcemodule HyperParameterTrainingJobEnvironmentKey = Awso_sagemaker.Values.HyperParameterTrainingJobEnvironmentKey
Sourcemodule HyperParameterTrainingJobEnvironmentMap = Awso_sagemaker.Values.HyperParameterTrainingJobEnvironmentMap
Sourcemodule HyperParameterTrainingJobDefinitionName = Awso_sagemaker.Values.HyperParameterTrainingJobDefinitionName
include module type of struct include Awso_sagemaker.Values_1 end
Sourcemodule HyperParameterAlgorithmSpecification = Awso_sagemaker.Values.HyperParameterAlgorithmSpecification

Specifies which training algorithm to use for training jobs that a hyperparameter tuning job launches and the metrics to monitor.

Sourcemodule HyperParameterTrainingJobDefinition = Awso_sagemaker.Values.HyperParameterTrainingJobDefinition

Defines the training jobs launched by a hyperparameter tuning job.

Sourcemodule HyperParameterTrainingJobDefinitions = Awso_sagemaker.Values.HyperParameterTrainingJobDefinitions
Sourcemodule CreateHyperParameterTuningJobRequest = Awso_sagemaker.Values.CreateHyperParameterTuningJobRequest

Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose. A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and trial components for each training job that it runs. You can view these entities in Amazon SageMaker Studio. For more information, see View Experiments, Trials, and Trial Components. Do not include any security-sensitive information including account access IDs, secrets, or tokens in any hyperparameter fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by any security-sensitive information included in the request hyperparameter variable or plain text fields..

Sourcemodule HyperParameterTuningJobArn = Awso_sagemaker.Values.HyperParameterTuningJobArn
Sourcemodule CreateHyperParameterTuningJobResponse = Awso_sagemaker.Values.CreateHyperParameterTuningJobResponse

Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose. A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and trial components for each training job that it runs. You can view these entities in Amazon SageMaker Studio. For more information, see View Experiments, Trials, and Trial Components. Do not include any security-sensitive information including account access IDs, secrets, or tokens in any hyperparameter fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by any security-sensitive information included in the request hyperparameter variable or plain text fields..

Creates a custom SageMaker AI image. A SageMaker AI image is a set of image versions. Each image version represents a container image stored in Amazon ECR. For more information, see Bring your own SageMaker AI image.

Creates a custom SageMaker AI image. A SageMaker AI image is a set of image versions. Each image version represents a container image stored in Amazon ECR. For more information, see Bring your own SageMaker AI image.

Sourcemodule SageMakerImageVersionAlias = Awso_sagemaker.Values.SageMakerImageVersionAlias
Sourcemodule SageMakerImageVersionAliases = Awso_sagemaker.Values.SageMakerImageVersionAliases

Creates a version of the SageMaker AI image specified by ImageName. The version represents the Amazon ECR container image specified by BaseImage.

Sourcemodule CreateImageVersionResponse = Awso_sagemaker.Values.CreateImageVersionResponse

Creates a version of the SageMaker AI image specified by ImageName. The version represents the Amazon ECR container image specified by BaseImage.

Sourcemodule InferenceComponentStartupParameters = Awso_sagemaker.Values.InferenceComponentStartupParameters

Settings that take effect while the model container starts up.

Sourcemodule InferenceComponentPlacementStrategy = Awso_sagemaker.Values.InferenceComponentPlacementStrategy
Sourcemodule InferenceComponentAvailabilityZoneBalance = Awso_sagemaker.Values.InferenceComponentAvailabilityZoneBalance

Configuration for balancing inference component copies across Availability Zones.

Sourcemodule InferenceComponentSchedulingConfig = Awso_sagemaker.Values.InferenceComponentSchedulingConfig

The scheduling configuration that determines how inference component copies are placed across available instances when copies are added or removed.

Sourcemodule InferenceComponentDataCacheConfig = Awso_sagemaker.Values.InferenceComponentDataCacheConfig

Settings that affect how the inference component caches data.

Sourcemodule InferenceComponentContainerSpecification = Awso_sagemaker.Values.InferenceComponentContainerSpecification

Defines a container that provides the runtime environment for a model that you deploy with an inference component.

Sourcemodule NumberOfAcceleratorDevices = Awso_sagemaker.Values.NumberOfAcceleratorDevices
Sourcemodule InferenceComponentComputeResourceRequirements = Awso_sagemaker.Values.InferenceComponentComputeResourceRequirements

Defines the compute resources to allocate to run a model, plus any adapter models, that you assign to an inference component. These resources include CPU cores, accelerators, and memory.

Sourcemodule InferenceComponentSpecification = Awso_sagemaker.Values.InferenceComponentSpecification

Details about the resources to deploy with this inference component, including the model, container, and compute resources.

Sourcemodule InferenceComponentSpecificationList = Awso_sagemaker.Values.InferenceComponentSpecificationList
Sourcemodule InferenceComponentCopyCount = Awso_sagemaker.Values.InferenceComponentCopyCount
Sourcemodule InferenceComponentRuntimeConfig = Awso_sagemaker.Values.InferenceComponentRuntimeConfig

Runtime settings for a model that is deployed with an inference component.

Sourcemodule CreateInferenceComponentInput = Awso_sagemaker.Values.CreateInferenceComponentInput

Creates an inference component, which is a SageMaker AI hosting object that you can use to deploy a model to an endpoint. In the inference component settings, you specify the model, the endpoint, and how the model utilizes the resources that the endpoint hosts. You can optimize resource utilization by tailoring how the required CPU cores, accelerators, and memory are allocated. You can deploy multiple inference components to an endpoint, where each inference component contains one model and the resource utilization needs for that individual model. After you deploy an inference component, you can directly invoke the associated model when you use the InvokeEndpoint API action.

Sourcemodule CreateInferenceComponentOutput = Awso_sagemaker.Values.CreateInferenceComponentOutput

Creates an inference component, which is a SageMaker AI hosting object that you can use to deploy a model to an endpoint. In the inference component settings, you specify the model, the endpoint, and how the model utilizes the resources that the endpoint hosts. You can optimize resource utilization by tailoring how the required CPU cores, accelerators, and memory are allocated. You can deploy multiple inference components to an endpoint, where each inference component contains one model and the resource utilization needs for that individual model. After you deploy an inference component, you can directly invoke the associated model when you use the InvokeEndpoint API action.

The name and sampling percentage of a shadow variant.

Sourcemodule ShadowModelVariantConfigList = Awso_sagemaker.Values.ShadowModelVariantConfigList

The configuration of ShadowMode inference experiment type, which specifies a production variant to take all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant it also specifies the percentage of requests that Amazon SageMaker replicates.

The infrastructure configuration for deploying the model to a real-time inference endpoint.

The configuration for the infrastructure that the model will be deployed to.

Contains information about the deployment options of a model.

Sourcemodule InferenceExperimentSchedule = Awso_sagemaker.Values.InferenceExperimentSchedule

The start and end times of an inference experiment. The maximum duration that you can set for an inference experiment is 30 days.

Sourcemodule InferenceExperimentDescription = Awso_sagemaker.Values.InferenceExperimentDescription
Sourcemodule InferenceExperimentDataStorageConfig = Awso_sagemaker.Values.InferenceExperimentDataStorageConfig

The Amazon S3 location and configuration for storing inference request and response data.

Sourcemodule CreateInferenceExperimentRequest = Awso_sagemaker.Values.CreateInferenceExperimentRequest

Creates an inference experiment using the configurations specified in the request. Use this API to setup and schedule an experiment to compare model variants on a Amazon SageMaker inference endpoint. For more information about inference experiments, see Shadow tests. Amazon SageMaker begins your experiment at the scheduled time and routes traffic to your endpoint's model variants based on your specified configuration. While the experiment is in progress or after it has concluded, you can view metrics that compare your model variants. For more information, see View, monitor, and edit shadow tests.

Sourcemodule CreateInferenceExperimentResponse = Awso_sagemaker.Values.CreateInferenceExperimentResponse

Creates an inference experiment using the configurations specified in the request. Use this API to setup and schedule an experiment to compare model variants on a Amazon SageMaker inference endpoint. For more information about inference experiments, see Shadow tests. Amazon SageMaker begins your experiment at the scheduled time and routes traffic to your endpoint's model variants based on your specified configuration. While the experiment is in progress or after it has concluded, you can view metrics that compare your model variants. For more information, see View, monitor, and edit shadow tests.

The model latency threshold.

Sourcemodule RecommendationJobStoppingConditions = Awso_sagemaker.Values.RecommendationJobStoppingConditions

Specifies conditions for stopping a job. When a job reaches a stopping condition limit, SageMaker ends the job.

Sourcemodule RecommendationJobCompiledOutputConfig = Awso_sagemaker.Values.RecommendationJobCompiledOutputConfig

Provides information about the output configuration for the compiled model.

Sourcemodule RecommendationJobOutputConfig = Awso_sagemaker.Values.RecommendationJobOutputConfig

Provides information about the output configuration for the compiled model.

Defines the stairs traffic pattern for an Inference Recommender load test. This pattern type consists of multiple steps where the number of users increases at each step. Specify either the stairs or phases traffic pattern.

Defines the traffic pattern.

Defines the traffic pattern of the load test.

Sourcemodule RecommendationJobVpcSubnetId = Awso_sagemaker.Values.RecommendationJobVpcSubnetId
Sourcemodule RecommendationJobVpcSubnets = Awso_sagemaker.Values.RecommendationJobVpcSubnets
Sourcemodule RecommendationJobVpcSecurityGroupId = Awso_sagemaker.Values.RecommendationJobVpcSecurityGroupId
Sourcemodule RecommendationJobVpcSecurityGroupIds = Awso_sagemaker.Values.RecommendationJobVpcSecurityGroupIds
Sourcemodule RecommendationJobVpcConfig = Awso_sagemaker.Values.RecommendationJobVpcConfig

Inference Recommender provisions SageMaker endpoints with access to VPC in the inference recommendation job.

Sourcemodule RecommendationJobResourceLimit = Awso_sagemaker.Values.RecommendationJobResourceLimit

Specifies the maximum number of jobs that can run in parallel and the maximum number of jobs that can run.

Sourcemodule RecommendationJobSupportedResponseMIMEType = Awso_sagemaker.Values.RecommendationJobSupportedResponseMIMEType
Sourcemodule RecommendationJobSupportedResponseMIMETypes = Awso_sagemaker.Values.RecommendationJobSupportedResponseMIMETypes
Sourcemodule RecommendationJobSupportedInstanceTypes = Awso_sagemaker.Values.RecommendationJobSupportedInstanceTypes
Sourcemodule RecommendationJobSupportedEndpointType = Awso_sagemaker.Values.RecommendationJobSupportedEndpointType
Sourcemodule RecommendationJobSupportedContentType = Awso_sagemaker.Values.RecommendationJobSupportedContentType
Sourcemodule RecommendationJobSupportedContentTypes = Awso_sagemaker.Values.RecommendationJobSupportedContentTypes
Sourcemodule RecommendationJobPayloadConfig = Awso_sagemaker.Values.RecommendationJobPayloadConfig

The configuration for the payload for a recommendation job.

Sourcemodule RecommendationJobFrameworkVersion = Awso_sagemaker.Values.RecommendationJobFrameworkVersion
Sourcemodule RecommendationJobDataInputConfig = Awso_sagemaker.Values.RecommendationJobDataInputConfig
Sourcemodule RecommendationJobContainerConfig = Awso_sagemaker.Values.RecommendationJobContainerConfig

Specifies mandatory fields for running an Inference Recommender job directly in the CreateInferenceRecommendationsJob API. The fields specified in ContainerConfig override the corresponding fields in the model package. Use ContainerConfig if you want to specify these fields for the recommendation job but don't want to edit them in your model package.

Details about a customer endpoint that was compared in an Inference Recommender job.

Sourcemodule EnvironmentParameterRanges = Awso_sagemaker.Values.EnvironmentParameterRanges

Specifies the range of environment parameters

Sourcemodule EndpointInputConfiguration = Awso_sagemaker.Values.EndpointInputConfiguration

The endpoint configuration for the load test.

Sourcemodule EndpointInputConfigurations = Awso_sagemaker.Values.EndpointInputConfigurations
Sourcemodule RecommendationJobInputConfig = Awso_sagemaker.Values.RecommendationJobInputConfig

The input configuration of the recommendation job.

Sourcemodule RecommendationJobDescription = Awso_sagemaker.Values.RecommendationJobDescription
Sourcemodule CreateInferenceRecommendationsJobRequest = Awso_sagemaker.Values.CreateInferenceRecommendationsJobRequest

Starts a recommendation job. You can create either an instance recommendation or load test job.

Sourcemodule CreateInferenceRecommendationsJobResponse = Awso_sagemaker.Values.CreateInferenceRecommendationsJobResponse

Starts a recommendation job. You can create either an instance recommendation or load test job.

Sourcemodule MaxPercentageOfInputDatasetLabeled = Awso_sagemaker.Values.MaxPercentageOfInputDatasetLabeled
Sourcemodule MaxHumanLabeledObjectCount = Awso_sagemaker.Values.MaxHumanLabeledObjectCount
Sourcemodule LabelingJobStoppingConditions = Awso_sagemaker.Values.LabelingJobStoppingConditions

A set of conditions for stopping a labeling job. If any of the conditions are met, the job is automatically stopped. You can use these conditions to control the cost of data labeling. Labeling jobs fail after 30 days with an appropriate client error message.

Output configuration information for a labeling job.

An Amazon SNS data source used for streaming labeling jobs.

The Amazon S3 location of the input data objects.

Provides information about the location of input data. You must specify at least one of the following: S3DataSource or SnsDataSource. Use SnsDataSource to specify an SNS input topic for a streaming labeling job. If you do not specify and SNS input topic ARN, Ground Truth will create a one-time labeling job. Use S3DataSource to specify an input manifest file for both streaming and one-time labeling jobs. Adding an S3DataSource is optional if you use SnsDataSource to create a streaming labeling job.

Attributes of the data specified by the customer. Use these to describe the data to be labeled.

Input configuration information for a labeling job.

Configure encryption on the storage volume attached to the ML compute instance used to run automated data labeling model training and inference.

Sourcemodule LabelingJobAlgorithmSpecificationArn = Awso_sagemaker.Values.LabelingJobAlgorithmSpecificationArn
Sourcemodule LabelingJobAlgorithmsConfig = Awso_sagemaker.Values.LabelingJobAlgorithmsConfig

Provides configuration information for auto-labeling of your data objects. A LabelingJobAlgorithmsConfig object must be supplied in order to use auto-labeling.

Provided configuration information for the worker UI for a labeling job. Provide either HumanTaskUiArn or UiTemplateS3Uri. For named entity recognition, 3D point cloud and video frame labeling jobs, use HumanTaskUiArn. For all other Ground Truth built-in task types and custom task types, use UiTemplateS3Uri to specify the location of a worker task template in Amazon S3.

Sourcemodule TaskAvailabilityLifetimeInSeconds = Awso_sagemaker.Values.TaskAvailabilityLifetimeInSeconds
Sourcemodule NumberOfHumanWorkersPerDataObject = Awso_sagemaker.Values.NumberOfHumanWorkersPerDataObject

Information required for human workers to complete a labeling task.

Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models. You can select your workforce from one of three providers: A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required. One or more vendors that you select from the Amazon Web Services Marketplace. Vendors provide expertise in specific areas. The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information. You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling. The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data. The output can be used as the manifest file for another labeling job or as training data for your machine learning models. You can use this operation to create a static labeling job or a streaming labeling job. A static labeling job stops if all data objects in the input manifest file identified in ManifestS3Uri have been labeled. A streaming labeling job runs perpetually until it is manually stopped, or remains idle for 10 days. You can send new data objects to an active (InProgress) streaming labeling job in real time. To learn how to create a static labeling job, see Create a Labeling Job (API) in the Amazon SageMaker Developer Guide. To learn how to create a streaming labeling job, see Create a Streaming Labeling Job.

Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models. You can select your workforce from one of three providers: A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required. One or more vendors that you select from the Amazon Web Services Marketplace. Vendors provide expertise in specific areas. The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information. You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling. The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data. The output can be used as the manifest file for another labeling job or as training data for your machine learning models. You can use this operation to create a static labeling job or a streaming labeling job. A static labeling job stops if all data objects in the input manifest file identified in ManifestS3Uri have been labeled. A streaming labeling job runs perpetually until it is manually stopped, or remains idle for 10 days. You can send new data objects to an active (InProgress) streaming labeling job in real time. To learn how to create a static labeling job, see Create a Labeling Job (API) in the Amazon SageMaker Developer Guide. To learn how to create a streaming labeling job, see Create a Streaming Labeling Job.

Sourcemodule WeeklyMaintenanceWindowStart = Awso_sagemaker.Values.WeeklyMaintenanceWindowStart

Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store.

Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store.

Sourcemodule CreateMlflowTrackingServerRequest = Awso_sagemaker.Values.CreateMlflowTrackingServerRequest

Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store. For more information, see Create an MLflow Tracking Server.

Sourcemodule CreateMlflowTrackingServerResponse = Awso_sagemaker.Values.CreateMlflowTrackingServerResponse

Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store. For more information, see Create an MLflow Tracking Server.

Sourcemodule MonitoringGroundTruthS3Input = Awso_sagemaker.Values.MonitoringGroundTruthS3Input

The ground truth labels for the dataset used for the monitoring job.

Inputs for the model bias job.

The configuration for a baseline model bias job.

Docker container image configuration object for the model bias job.

Sourcemodule CreateModelBiasJobDefinitionRequest = Awso_sagemaker.Values.CreateModelBiasJobDefinitionRequest

Creates the definition for a model bias job.

Sourcemodule CreateModelBiasJobDefinitionResponse = Awso_sagemaker.Values.CreateModelBiasJobDefinitionResponse

Creates the definition for a model bias job.

Sourcemodule ModelCardExportOutputConfig = Awso_sagemaker.Values.ModelCardExportOutputConfig

Configure the export output details for an Amazon SageMaker Model Card.

Sourcemodule CreateModelCardExportJobRequest = Awso_sagemaker.Values.CreateModelCardExportJobRequest

Creates an Amazon SageMaker Model Card export job.

Sourcemodule CreateModelCardExportJobResponse = Awso_sagemaker.Values.CreateModelCardExportJobResponse

Creates an Amazon SageMaker Model Card export job.

Configure the security settings to protect model card data.

Creates an Amazon SageMaker Model Card. For information about how to use model cards, see Amazon SageMaker Model Card.

Creates an Amazon SageMaker Model Card. For information about how to use model cards, see Amazon SageMaker Model Card.

Sourcemodule ModelExplainabilityJobInput = Awso_sagemaker.Values.ModelExplainabilityJobInput

Inputs for the model explainability job.

Sourcemodule ModelExplainabilityBaselineConfig = Awso_sagemaker.Values.ModelExplainabilityBaselineConfig

The configuration for a baseline model explainability job.

Sourcemodule ModelExplainabilityAppSpecification = Awso_sagemaker.Values.ModelExplainabilityAppSpecification

Docker container image configuration object for the model explainability job.

Sourcemodule CreateModelExplainabilityJobDefinitionRequest = Awso_sagemaker.Values.CreateModelExplainabilityJobDefinitionRequest

Creates the definition for a model explainability job.

Sourcemodule CreateModelExplainabilityJobDefinitionResponse = Awso_sagemaker.Values.CreateModelExplainabilityJobDefinitionResponse

Creates the definition for a model explainability job.

Specifies details about how containers in a multi-container endpoint are run.

Creates a model in SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions. Use this API to create a model if you want to use SageMaker hosting services or run a batch transform job. To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then create an endpoint with the CreateEndpoint API. SageMaker then deploys all of the containers that you defined for the model in the hosting environment. To run a batch transform using your model, you start a job with the CreateTransformJob API. SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location. In the request, you also provide an IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other Amazon Web Services resources, you grant necessary permissions via this role.

Creates a model in SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions. Use this API to create a model if you want to use SageMaker hosting services or run a batch transform job. To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then create an endpoint with the CreateEndpoint API. SageMaker then deploys all of the containers that you defined for the model in the hosting environment. To run a batch transform using your model, you start a job with the CreateTransformJob API. SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location. In the request, you also provide an IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other Amazon Web Services resources, you grant necessary permissions via this role.

The managed configuration of a model package group.

Sourcemodule CreateModelPackageGroupInput = Awso_sagemaker.Values.CreateModelPackageGroupInput

Creates a model group. A model group contains a group of model versions.

Sourcemodule CreateModelPackageGroupOutput = Awso_sagemaker.Values.CreateModelPackageGroupOutput

Creates a model group. A model group contains a group of model versions.

Specifies an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.

Sourcemodule SourceAlgorithmSpecification = Awso_sagemaker.Values.SourceAlgorithmSpecification

A list of algorithms that were used to create a model package.

Sourcemodule ModelPackageValidationProfile = Awso_sagemaker.Values.ModelPackageValidationProfile

Contains data, such as the inputs and targeted instance types that are used in the process of validating the model package. The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.

Sourcemodule ModelPackageValidationProfiles = Awso_sagemaker.Values.ModelPackageValidationProfiles
Sourcemodule ModelPackageValidationSpecification = Awso_sagemaker.Values.ModelPackageValidationSpecification

Specifies batch transform jobs that SageMaker runs to validate your model package.

Sourcemodule ModelPackageSecurityConfig = Awso_sagemaker.Values.ModelPackageSecurityConfig

An optional Key Management Service key to encrypt, decrypt, and re-encrypt model package information for regulated workloads with highly sensitive data.

The model card associated with the model package. Since ModelPackageModelCard is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema of ModelCard. The ModelPackageModelCard schema does not include model_package_details, and model_overview is composed of the model_creator and model_artifact properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.

Model quality statistics and constraints.

Data quality constraints and statistics for a model.

Contains explainability metrics for a model.

Contains metrics captured from a model.

A structure describing the current state of the model in its life cycle.

Represents the drift check model quality baselines that can be used when the model monitor is set using the model package.

Sourcemodule DriftCheckModelDataQuality = Awso_sagemaker.Values.DriftCheckModelDataQuality

Represents the drift check data quality baselines that can be used when the model monitor is set using the model package.

Contains details regarding the file source.

Represents the drift check explainability baselines that can be used when the model monitor is set using the model package.

Represents the drift check bias baselines that can be used when the model monitor is set using the model package.

Represents the drift check baselines that can be used when the model monitor is set using the model package.

Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker. To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for InferenceSpecification. To create a model from an algorithm resource that you created or subscribed to in Amazon Web Services Marketplace, provide a value for SourceAlgorithmSpecification. There are two types of model packages: Versioned - a model that is part of a model group in the model registry. Unversioned - a model package that is not part of a model group.

Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker. To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for InferenceSpecification. To create a model from an algorithm resource that you created or subscribed to in Amazon Web Services Marketplace, provide a value for SourceAlgorithmSpecification. There are two types of model packages: Versioned - a model that is part of a model group in the model registry. Unversioned - a model package that is not part of a model group.

The input for the model quality monitoring job. Currently endpoints are supported for input for model quality monitoring jobs.

Sourcemodule ModelQualityBaselineConfig = Awso_sagemaker.Values.ModelQualityBaselineConfig

Configuration for monitoring constraints and monitoring statistics. These baseline resources are compared against the results of the current job from the series of jobs scheduled to collect data periodically.

Sourcemodule ModelQualityAppSpecification = Awso_sagemaker.Values.ModelQualityAppSpecification

Container image configuration object for the monitoring job.

Sourcemodule CreateModelQualityJobDefinitionRequest = Awso_sagemaker.Values.CreateModelQualityJobDefinitionRequest

Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker AI Model Monitor.

Sourcemodule CreateModelQualityJobDefinitionResponse = Awso_sagemaker.Values.CreateModelQualityJobDefinitionResponse

Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker AI Model Monitor.

Configuration details about the monitoring schedule.

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

The inputs for a monitoring job.

Configuration for monitoring constraints and monitoring statistics. These baseline resources are compared against the results of the current job from the series of jobs scheduled to collect data periodically.

Sourcemodule MonitoringAppSpecification = Awso_sagemaker.Values.MonitoringAppSpecification

Container image configuration object for the monitoring job.

Defines the monitoring job.

Configures the monitoring schedule and defines the monitoring job.

Sourcemodule CreateMonitoringScheduleRequest = Awso_sagemaker.Values.CreateMonitoringScheduleRequest

Creates a schedule that regularly starts Amazon SageMaker AI Processing Jobs to monitor the data captured for an Amazon SageMaker AI Endpoint.

Sourcemodule CreateMonitoringScheduleResponse = Awso_sagemaker.Values.CreateMonitoringScheduleResponse

Creates a schedule that regularly starts Amazon SageMaker AI Processing Jobs to monitor the data captured for an Amazon SageMaker AI Endpoint.

Sourcemodule NotebookInstanceVolumeSizeInGB = Awso_sagemaker.Values.NotebookInstanceVolumeSizeInGB
Sourcemodule NotebookInstanceLifecycleConfigName = Awso_sagemaker.Values.NotebookInstanceLifecycleConfigName
Sourcemodule NotebookInstanceAcceleratorType = Awso_sagemaker.Values.NotebookInstanceAcceleratorType
Sourcemodule NotebookInstanceAcceleratorTypes = Awso_sagemaker.Values.NotebookInstanceAcceleratorTypes
Sourcemodule MinimumInstanceMetadataServiceVersion = Awso_sagemaker.Values.MinimumInstanceMetadataServiceVersion
Sourcemodule InstanceMetadataServiceConfiguration = Awso_sagemaker.Values.InstanceMetadataServiceConfiguration

Information on the IMDS configuration of the notebook instance

Sourcemodule CreateNotebookInstanceInput = Awso_sagemaker.Values.CreateNotebookInstanceInput

Creates an SageMaker AI notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook. In a CreateNotebookInstance request, specify the type of ML compute instance that you want to run. SageMaker AI launches the instance, installs common libraries that you can use to explore datasets for model training, and attaches an ML storage volume to the notebook instance. SageMaker AI also provides a set of example notebooks. Each notebook demonstrates how to use SageMaker AI with a specific algorithm or with a machine learning framework. After receiving the request, SageMaker AI does the following: Creates a network interface in the SageMaker AI VPC. (Option) If you specified SubnetId, SageMaker AI creates a network interface in your own VPC, which is inferred from the subnet ID that you provide in the input. When creating this network interface, SageMaker AI attaches the security group that you specified in the request to the network interface that it creates in your VPC. Launches an EC2 instance of the type specified in the request in the SageMaker AI VPC. If you specified SubnetId of your VPC, SageMaker AI specifies both network interfaces when launching this instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security groups allow it. After creating the notebook instance, SageMaker AI returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it. After SageMaker AI creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating SageMaker AI endpoints, and validate hosted models. For more information, see How It Works.

Sourcemodule NotebookInstanceLifecycleConfigContent = Awso_sagemaker.Values.NotebookInstanceLifecycleConfigContent
Sourcemodule NotebookInstanceLifecycleHook = Awso_sagemaker.Values.NotebookInstanceLifecycleHook

Contains the notebook instance lifecycle configuration script. Each lifecycle configuration script has a limit of 16384 characters. The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin. View Amazon CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook]. Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started. For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

Sourcemodule NotebookInstanceLifecycleConfigList = Awso_sagemaker.Values.NotebookInstanceLifecycleConfigList
Sourcemodule CreateNotebookInstanceLifecycleConfigInput = Awso_sagemaker.Values.CreateNotebookInstanceLifecycleConfigInput

Creates a lifecycle configuration that you can associate with a notebook instance. A lifecycle configuration is a collection of shell scripts that run when you create or start a notebook instance. Each lifecycle configuration script has a limit of 16384 characters. The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin. View Amazon CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook]. Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started. For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance. Lifecycle configuration scripts execute with root access and the notebook instance's IAM execution role privileges. Grant this permission only to trusted principals. See Customize a Notebook Instance Using a Lifecycle Configuration Script for security best practices.

Sourcemodule NotebookInstanceLifecycleConfigArn = Awso_sagemaker.Values.NotebookInstanceLifecycleConfigArn
Sourcemodule CreateNotebookInstanceLifecycleConfigOutput = Awso_sagemaker.Values.CreateNotebookInstanceLifecycleConfigOutput

Creates a lifecycle configuration that you can associate with a notebook instance. A lifecycle configuration is a collection of shell scripts that run when you create or start a notebook instance. Each lifecycle configuration script has a limit of 16384 characters. The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin. View Amazon CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook]. Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started. For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance. Lifecycle configuration scripts execute with root access and the notebook instance's IAM execution role privileges. Grant this permission only to trusted principals. See Customize a Notebook Instance Using a Lifecycle Configuration Script for security best practices.

Sourcemodule CreateNotebookInstanceOutput = Awso_sagemaker.Values.CreateNotebookInstanceOutput

Creates an SageMaker AI notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook. In a CreateNotebookInstance request, specify the type of ML compute instance that you want to run. SageMaker AI launches the instance, installs common libraries that you can use to explore datasets for model training, and attaches an ML storage volume to the notebook instance. SageMaker AI also provides a set of example notebooks. Each notebook demonstrates how to use SageMaker AI with a specific algorithm or with a machine learning framework. After receiving the request, SageMaker AI does the following: Creates a network interface in the SageMaker AI VPC. (Option) If you specified SubnetId, SageMaker AI creates a network interface in your own VPC, which is inferred from the subnet ID that you provide in the input. When creating this network interface, SageMaker AI attaches the security group that you specified in the request to the network interface that it creates in your VPC. Launches an EC2 instance of the type specified in the request in the SageMaker AI VPC. If you specified SubnetId of your VPC, SageMaker AI specifies both network interfaces when launching this instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security groups allow it. After creating the notebook instance, SageMaker AI returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it. After SageMaker AI creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating SageMaker AI endpoints, and validate hosted models. For more information, see How It Works.

Sourcemodule OptimizationVpcSecurityGroupId = Awso_sagemaker.Values.OptimizationVpcSecurityGroupId
Sourcemodule OptimizationVpcSecurityGroupIds = Awso_sagemaker.Values.OptimizationVpcSecurityGroupIds

A VPC in Amazon VPC that's accessible to an optimized that you create with an optimization job. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.

Sourcemodule OptimizationSageMakerModel = Awso_sagemaker.Values.OptimizationSageMakerModel

A SageMaker model to use as the source or destination for an optimization job.

Sourcemodule OptimizationJobOutputConfig = Awso_sagemaker.Values.OptimizationJobOutputConfig

Details for where to store the optimized model that you create with the optimization job.

Sourcemodule OptimizationModelAcceptEula = Awso_sagemaker.Values.OptimizationModelAcceptEula
Sourcemodule OptimizationModelAccessConfig = Awso_sagemaker.Values.OptimizationModelAccessConfig

The access configuration settings for the source ML model for an optimization job, where you can accept the model end-user license agreement (EULA).

Sourcemodule OptimizationJobModelSourceS3 = Awso_sagemaker.Values.OptimizationJobModelSourceS3

The Amazon S3 location of a source model to optimize with an optimization job.

Sourcemodule OptimizationJobModelSource = Awso_sagemaker.Values.OptimizationJobModelSource

The location of the source model to optimize with an optimization job.

Sourcemodule OptimizationJobMaxInstanceCount = Awso_sagemaker.Values.OptimizationJobMaxInstanceCount
Sourcemodule OptimizationJobEnvironmentVariables = Awso_sagemaker.Values.OptimizationJobEnvironmentVariables
Sourcemodule OptimizationJobDeploymentInstanceType = Awso_sagemaker.Values.OptimizationJobDeploymentInstanceType
Sourcemodule ModelSpeculativeDecodingS3DataType = Awso_sagemaker.Values.ModelSpeculativeDecodingS3DataType
Sourcemodule ModelSpeculativeDecodingTrainingDataSource = Awso_sagemaker.Values.ModelSpeculativeDecodingTrainingDataSource

Contains information about the training data source for speculative decoding.

Sourcemodule ModelSpeculativeDecodingTechnique = Awso_sagemaker.Values.ModelSpeculativeDecodingTechnique
Sourcemodule ModelSpeculativeDecodingConfig = Awso_sagemaker.Values.ModelSpeculativeDecodingConfig

Settings for the model speculative decoding technique that's applied by a model optimization job.

Sourcemodule OptimizationContainerImage = Awso_sagemaker.Values.OptimizationContainerImage

Settings for the model sharding technique that's applied by a model optimization job.

Settings for the model quantization technique that's applied by a model optimization job.

Settings for the model compilation technique that's applied by a model optimization job.

Settings for an optimization technique that you apply with a model optimization job.

Sourcemodule CreateOptimizationJobRequest = Awso_sagemaker.Values.CreateOptimizationJobRequest

Creates a job that optimizes a model for inference performance. To create the job, you provide the location of a source model, and you provide the settings for the optimization techniques that you want the job to apply. When the job completes successfully, SageMaker uploads the new optimized model to the output destination that you specify. For more information about how to use this action, and about the supported optimization techniques, see Optimize model inference with Amazon SageMaker.

Sourcemodule CreateOptimizationJobResponse = Awso_sagemaker.Values.CreateOptimizationJobResponse

Creates a job that optimizes a model for inference performance. To create the job, you provide the location of a source model, and you provide the settings for the optimization techniques that you want the job to apply. When the job completes successfully, SageMaker uploads the new optimized model to the output destination that you specify. For more information about how to use this action, and about the supported optimization techniques, see Optimize model inference with Amazon SageMaker.

Sourcemodule SessionExpirationDurationInSeconds = Awso_sagemaker.Values.SessionExpirationDurationInSeconds
Sourcemodule CreatePartnerAppPresignedUrlRequest = Awso_sagemaker.Values.CreatePartnerAppPresignedUrlRequest

Creates a presigned URL to access an Amazon SageMaker Partner AI App.

Sourcemodule CreatePartnerAppPresignedUrlResponse = Awso_sagemaker.Values.CreatePartnerAppPresignedUrlResponse

Creates a presigned URL to access an Amazon SageMaker Partner AI App.

Sourcemodule PartnerAppMaintenanceConfig = Awso_sagemaker.Values.PartnerAppMaintenanceConfig

Maintenance configuration settings for the SageMaker Partner AI App.

Defines the mapping between an in-app role and the Amazon Web Services IAM Identity Center group patterns that should be assigned to that role within the SageMaker Partner AI App.

Configuration settings for the SageMaker Partner AI App.

Creates an Amazon SageMaker Partner AI App.

Creates an Amazon SageMaker Partner AI App.

Sourcemodule PipelineDefinitionS3Location = Awso_sagemaker.Values.PipelineDefinitionS3Location

The location of the pipeline definition stored in Amazon S3.

Configuration that controls the parallelism of the pipeline. By default, the parallelism configuration specified applies to all executions of the pipeline unless overridden.

Creates a pipeline using a JSON pipeline definition.

Creates a pipeline using a JSON pipeline definition.

Sourcemodule CreatePresignedDomainUrlRequest = Awso_sagemaker.Values.CreatePresignedDomainUrlRequest

Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to the domain, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System volume. This operation can only be called when the authentication mode equals IAM. The IAM role or user passed to this API defines the permissions to access the app. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the app. You can restrict access to this API and to the URL that it returns to a list of IP addresses, Amazon VPCs or Amazon VPC Endpoints that you specify. For more information, see Connect to Amazon SageMaker AI Studio Through an Interface VPC Endpoint . The URL that you get from a call to CreatePresignedDomainUrl has a default timeout of 5 minutes. You can configure this value using ExpiresInSeconds. If you try to use the URL after the timeout limit expires, you are directed to the Amazon Web Services console sign-in page. The JupyterLab session default expiration time is 12 hours. You can configure this value using SessionExpirationDurationInSeconds.

Sourcemodule CreatePresignedDomainUrlResponse = Awso_sagemaker.Values.CreatePresignedDomainUrlResponse

Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to the domain, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System volume. This operation can only be called when the authentication mode equals IAM. The IAM role or user passed to this API defines the permissions to access the app. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the app. You can restrict access to this API and to the URL that it returns to a list of IP addresses, Amazon VPCs or Amazon VPC Endpoints that you specify. For more information, see Connect to Amazon SageMaker AI Studio Through an Interface VPC Endpoint . The URL that you get from a call to CreatePresignedDomainUrl has a default timeout of 5 minutes. You can configure this value using ExpiresInSeconds. If you try to use the URL after the timeout limit expires, you are directed to the Amazon Web Services console sign-in page. The JupyterLab session default expiration time is 12 hours. You can configure this value using SessionExpirationDurationInSeconds.

Sourcemodule CreatePresignedMlflowAppUrlRequest = Awso_sagemaker.Values.CreatePresignedMlflowAppUrlRequest

Returns a presigned URL that you can use to connect to the MLflow UI attached to your MLflow App. For more information, see Launch the MLflow UI using a presigned URL.

Sourcemodule CreatePresignedMlflowAppUrlResponse = Awso_sagemaker.Values.CreatePresignedMlflowAppUrlResponse

Returns a presigned URL that you can use to connect to the MLflow UI attached to your MLflow App. For more information, see Launch the MLflow UI using a presigned URL.

Sourcemodule CreatePresignedMlflowTrackingServerUrlRequest = Awso_sagemaker.Values.CreatePresignedMlflowTrackingServerUrlRequest

Returns a presigned URL that you can use to connect to the MLflow UI attached to your tracking server. For more information, see Launch the MLflow UI using a presigned URL.

Sourcemodule CreatePresignedMlflowTrackingServerUrlResponse = Awso_sagemaker.Values.CreatePresignedMlflowTrackingServerUrlResponse

Returns a presigned URL that you can use to connect to the MLflow UI attached to your tracking server. For more information, see Launch the MLflow UI using a presigned URL.

Sourcemodule CreatePresignedNotebookInstanceUrlInput = Awso_sagemaker.Values.CreatePresignedNotebookInstanceUrlInput

Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the SageMaker AI console, when you choose Open next to a notebook instance, SageMaker AI opens a new tab showing the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page. The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance. You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. Use the NotIpAddress condition operator and the aws:SourceIP condition context key to specify the list of IP addresses that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address. The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the Amazon Web Services console sign-in page.

Sourcemodule CreatePresignedNotebookInstanceUrlOutput = Awso_sagemaker.Values.CreatePresignedNotebookInstanceUrlOutput

Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the SageMaker AI console, when you choose Open next to a notebook instance, SageMaker AI opens a new tab showing the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page. The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance. You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. Use the NotIpAddress condition operator and the aws:SourceIP condition context key to specify the list of IP addresses that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address. The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the Amazon Web Services console sign-in page.

Sourcemodule ProcessingMaxRuntimeInSeconds = Awso_sagemaker.Values.ProcessingMaxRuntimeInSeconds
Sourcemodule ProcessingStoppingCondition = Awso_sagemaker.Values.ProcessingStoppingCondition

Configures conditions under which the processing job should be stopped, such as how long the processing job has been running. After the condition is met, the processing job is stopped.

Configuration for the cluster used to run a processing job.

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.

Configuration for uploading output data to Amazon S3 from the processing container.

Sourcemodule ProcessingFeatureStoreOutput = Awso_sagemaker.Values.ProcessingFeatureStoreOutput

Configuration for processing job outputs in Amazon SageMaker Feature Store.

Describes the results of a processing job. The processing output must specify exactly one of either S3Output or FeatureStoreOutput types.

Configuration for uploading output from the processing container.

Sourcemodule ProcessingS3CompressionType = Awso_sagemaker.Values.ProcessingS3CompressionType

Configuration for downloading input data from Amazon S3 into the processing container.

The database user name used in Redshift query execution.

Sourcemodule RedshiftResultCompressionType = Awso_sagemaker.Values.RedshiftResultCompressionType

The SQL query statements to be executed.

The name of the Redshift database used in Redshift query execution.

The Redshift cluster Identifier.

Configuration for Redshift Dataset Definition input.

Configuration for Dataset Definition inputs. The Dataset Definition input must specify exactly one of either AthenaDatasetDefinition or RedshiftDatasetDefinition types.

The inputs for a processing job. The processing input must specify exactly one of either S3Input or DatasetDefinition types.

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

Sourcemodule CreateProcessingJobRequest = Awso_sagemaker.Values.CreateProcessingJobRequest

Creates a processing job.

Sourcemodule CreateProcessingJobResponse = Awso_sagemaker.Values.CreateProcessingJobResponse

Creates a processing job.

Sourcemodule ProvisioningParameterValue = Awso_sagemaker.Values.ProvisioningParameterValue

A key value pair used when you provision a project as a service catalog product. For information, see What is Amazon Web Services Service Catalog.

Sourcemodule ServiceCatalogProvisioningDetails = Awso_sagemaker.Values.ServiceCatalogProvisioningDetails

Details that you specify to provision a service catalog product. For information about service catalog, see What is Amazon Web Services Service Catalog.

Contains configuration details for a template provider. Only one type of template provider can be specified.

Sourcemodule CreateTemplateProviderList = Awso_sagemaker.Values.CreateTemplateProviderList

Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model.

Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model.

A collection of space sharing settings.

A collection of EBS storage settings that apply to both private and shared spaces.

The storage settings for a space.

Settings related to idle shutdown of Studio applications in a space.

Sourcemodule SpaceAppLifecycleManagement = Awso_sagemaker.Values.SpaceAppLifecycleManagement

Settings that are used to configure and manage the lifecycle of Amazon SageMaker Studio applications in a space.

Sourcemodule SpaceJupyterLabAppSettings = Awso_sagemaker.Values.SpaceJupyterLabAppSettings

The settings for the JupyterLab application within a space.

Sourcemodule SpaceCodeEditorAppSettings = Awso_sagemaker.Values.SpaceCodeEditorAppSettings

The application settings for a Code Editor space.

A custom file system in Amazon S3. This is only supported in Amazon SageMaker Unified Studio.

A custom file system in Amazon FSx for Lustre.

A file system, created by you in Amazon EFS, that you assign to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.

A file system, created by you, that you assign to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio.

A collection of space settings.

The collection of ownership settings for a space.

Creates a private space or a space used for real time collaboration in a domain.

Creates a private space or a space used for real time collaboration in a domain.

Sourcemodule StudioLifecycleConfigContent = Awso_sagemaker.Values.StudioLifecycleConfigContent
Sourcemodule StudioLifecycleConfigAppType = Awso_sagemaker.Values.StudioLifecycleConfigAppType
Sourcemodule CreateStudioLifecycleConfigRequest = Awso_sagemaker.Values.CreateStudioLifecycleConfigRequest

Creates a new Amazon SageMaker AI Studio Lifecycle Configuration.

Sourcemodule CreateStudioLifecycleConfigResponse = Awso_sagemaker.Values.CreateStudioLifecycleConfigResponse

Creates a new Amazon SageMaker AI Studio Lifecycle Configuration.

Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.

Contains information about attribute-based access control (ABAC) for a training job. The session chaining configuration uses Amazon Security Token Service (STS) for your training job to request temporary, limited-privilege credentials to tenants. For more information, see Attribute-based access control (ABAC) for multi-tenancy training.

ServerlessJobConfig relevant fields

The configuration for the serverless training job.

Configuration for remote debugging for the CreateTrainingJob API. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.

Configuration information for profiling rules.

Sourcemodule ProfilerRuleConfigurations = Awso_sagemaker.Values.ProfilerRuleConfigurations
Sourcemodule ProfilingIntervalInMilliseconds = Awso_sagemaker.Values.ProfilingIntervalInMilliseconds

Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.

The configuration for the Model package.

MlflowConfig relevant fields

MlflowDetails relevant fields

The MLflow configuration using SageMaker managed MLflow.

Configuration information for the infrastructure health check of a training job. A SageMaker-provided health check tests the health of instance hardware and cluster network connectivity.

Configuration information for SageMaker Debugger rules for debugging. To learn more about how to configure the DebugRuleConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.

Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.

Starts a model training job. After training completes, SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify. If you choose to host your model using SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than SageMaker, provided that you know how to use them for inference. In the request body, you provide the following: AlgorithmSpecification - Identifies the training algorithm to use. HyperParameters - Specify these algorithm-specific parameters to enable the estimation of model parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms. Do not include any security-sensitive information including account access IDs, secrets, or tokens in any hyperparameter fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request hyperparameter variable or plain text fields. InputDataConfig - Describes the input required by the training job and the Amazon S3, EFS, or FSx location where it is stored. OutputDataConfig - Identifies the Amazon S3 bucket where you want SageMaker to save the results of model training. ResourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance. EnableManagedSpotTraining - Optimize the cost of training machine learning models by up to 80% by using Amazon EC2 Spot instances. For more information, see Managed Spot Training. RoleArn - The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that SageMaker can successfully complete model training. StoppingCondition - To help cap training costs, use MaxRuntimeInSeconds to set a time limit for training. Use MaxWaitTimeInSeconds to specify how long a managed spot training job has to complete. Environment - The environment variables to set in the Docker container. Do not include any security-sensitive information including account access IDs, secrets, or tokens in any environment fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request environment variable or plain text fields. RetryStrategy - The number of times to retry the job when the job fails due to an InternalServerError. For more information about SageMaker, see How It Works.

Starts a model training job. After training completes, SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify. If you choose to host your model using SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than SageMaker, provided that you know how to use them for inference. In the request body, you provide the following: AlgorithmSpecification - Identifies the training algorithm to use. HyperParameters - Specify these algorithm-specific parameters to enable the estimation of model parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms. Do not include any security-sensitive information including account access IDs, secrets, or tokens in any hyperparameter fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request hyperparameter variable or plain text fields. InputDataConfig - Describes the input required by the training job and the Amazon S3, EFS, or FSx location where it is stored. OutputDataConfig - Identifies the Amazon S3 bucket where you want SageMaker to save the results of model training. ResourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance. EnableManagedSpotTraining - Optimize the cost of training machine learning models by up to 80% by using Amazon EC2 Spot instances. For more information, see Managed Spot Training. RoleArn - The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that SageMaker can successfully complete model training. StoppingCondition - To help cap training costs, use MaxRuntimeInSeconds to set a time limit for training. Use MaxWaitTimeInSeconds to specify how long a managed spot training job has to complete. Environment - The environment variables to set in the Docker container. Do not include any security-sensitive information including account access IDs, secrets, or tokens in any environment fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request environment variable or plain text fields. RetryStrategy - The number of times to retry the job when the job fails due to an InternalServerError. For more information about SageMaker, see How It Works.

Sourcemodule SpareInstanceCountPerUltraServer = Awso_sagemaker.Values.SpareInstanceCountPerUltraServer

Creates a new training plan in SageMaker to reserve compute capacity. Amazon SageMaker Training Plan is a capability within SageMaker that allows customers to reserve and manage GPU capacity for large-scale AI model training. It provides a way to secure predictable access to computational resources within specific timelines and budgets, without the need to manage underlying infrastructure. How it works Plans can be created for specific resources such as SageMaker Training Jobs or SageMaker HyperPod clusters, automatically provisioning resources, setting up infrastructure, executing workloads, and handling infrastructure failures. Plan creation workflow Users search for available plan offerings based on their requirements (e.g., instance type, count, start time, duration) using the SearchTrainingPlanOfferings API operation. They create a plan that best matches their needs using the ID of the plan offering they want to use. After successful upfront payment, the plan's status becomes Scheduled. The plan can be used to: Queue training jobs. Allocate to an instance group of a SageMaker HyperPod cluster. When the plan start date arrives, it becomes Active. Based on available reserved capacity: Training jobs are launched. Instance groups are provisioned. Plan composition A plan can consist of one or more Reserved Capacities, each defined by a specific instance type, quantity, Availability Zone, duration, and start and end times. For more information about Reserved Capacity, see ReservedCapacitySummary .

Sourcemodule CreateTrainingPlanResponse = Awso_sagemaker.Values.CreateTrainingPlanResponse

Creates a new training plan in SageMaker to reserve compute capacity. Amazon SageMaker Training Plan is a capability within SageMaker that allows customers to reserve and manage GPU capacity for large-scale AI model training. It provides a way to secure predictable access to computational resources within specific timelines and budgets, without the need to manage underlying infrastructure. How it works Plans can be created for specific resources such as SageMaker Training Jobs or SageMaker HyperPod clusters, automatically provisioning resources, setting up infrastructure, executing workloads, and handling infrastructure failures. Plan creation workflow Users search for available plan offerings based on their requirements (e.g., instance type, count, start time, duration) using the SearchTrainingPlanOfferings API operation. They create a plan that best matches their needs using the ID of the plan offering they want to use. After successful upfront payment, the plan's status becomes Scheduled. The plan can be used to: Queue training jobs. Allocate to an instance group of a SageMaker HyperPod cluster. When the plan start date arrives, it becomes Active. Based on available reserved capacity: Training jobs are launched. Instance groups are provisioned. Plan composition A plan can consist of one or more Reserved Capacities, each defined by a specific instance type, quantity, Availability Zone, duration, and start and end times. For more information about Reserved Capacity, see ReservedCapacitySummary .

Sourcemodule InvocationsTimeoutInSeconds = Awso_sagemaker.Values.InvocationsTimeoutInSeconds

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

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.

Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify. To perform batch transformations, you create a transform job and use the data that you have readily available. In the request body, you provide the following: TransformJobName - Identifies the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. ModelName - Identifies the model to use. ModelName must be the name of an existing Amazon SageMaker model in the same Amazon Web Services Region and Amazon Web Services account. For information on creating a model, see CreateModel. TransformInput - Describes the dataset to be transformed and the Amazon S3 location where it is stored. TransformOutput - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job. TransformResources - Identifies the ML compute instances and AMI image versions for the transform job. For more information about how batch transformation works, see Batch Transform.

Sourcemodule CreateTransformJobResponse = Awso_sagemaker.Values.CreateTransformJobResponse

Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify. To perform batch transformations, you create a transform job and use the data that you have readily available. In the request body, you provide the following: TransformJobName - Identifies the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. ModelName - Identifies the model to use. ModelName must be the name of an existing Amazon SageMaker model in the same Amazon Web Services Region and Amazon Web Services account. For information on creating a model, see CreateModel. TransformInput - Describes the dataset to be transformed and the Amazon S3 location where it is stored. TransformOutput - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job. TransformResources - Identifies the ML compute instances and AMI image versions for the transform job. For more information about how batch transformation works, see Batch Transform.

Sourcemodule TrialComponentStatusMessage = Awso_sagemaker.Values.TrialComponentStatusMessage
Sourcemodule TrialComponentPrimaryStatus = Awso_sagemaker.Values.TrialComponentPrimaryStatus

The status of the trial component.

Sourcemodule TrialComponentParameterValue = Awso_sagemaker.Values.TrialComponentParameterValue

The value of a hyperparameter. Only one of NumberValue or StringValue can be specified. This object is specified in the CreateTrialComponent request.

Sourcemodule TrialComponentArtifactValue = Awso_sagemaker.Values.TrialComponentArtifactValue

Represents an input or output artifact of a trial component. You specify TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts parameters in the CreateTrialComponent request. Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.

Sourcemodule CreateTrialComponentRequest = Awso_sagemaker.Values.CreateTrialComponentRequest

Creates a trial component, which is a stage of a machine learning trial. A trial is composed of one or more trial components. A trial component can be used in multiple trials. Trial components include pre-processing jobs, training jobs, and batch transform jobs. When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK. You can add tags to a trial component and then use the Search API to search for the tags.

Sourcemodule CreateTrialComponentResponse = Awso_sagemaker.Values.CreateTrialComponentResponse

Creates a trial component, which is a stage of a machine learning trial. A trial is composed of one or more trial components. A trial component can be used in multiple trials. Trial components include pre-processing jobs, training jobs, and batch transform jobs. When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK. You can add tags to a trial component and then use the Search API to search for the tags.

Creates an SageMaker trial. A trial is a set of steps called trial components that produce a machine learning model. A trial is part of a single SageMaker experiment. When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK. You can add tags to a trial and then use the Search API to search for the tags. To get a list of all your trials, call the ListTrials API. To view a trial's properties, call the DescribeTrial API. To create a trial component, call the CreateTrialComponent API.

Creates an SageMaker trial. A trial is a set of steps called trial components that produce a machine learning model. A trial is part of a single SageMaker experiment. When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK. You can add tags to a trial and then use the Search API to search for the tags. To get a list of all your trials, call the ListTrials API. To view a trial's properties, call the DescribeTrial API. To create a trial component, call the CreateTrialComponent API.

Sourcemodule SingleSignOnUserIdentifier = Awso_sagemaker.Values.SingleSignOnUserIdentifier

Creates a user profile. A user profile represents a single user within a domain, and is the main way to reference a "person" for the purposes of sharing, reporting, and other user-oriented features. This entity is created when a user onboards to a domain. If an administrator invites a person by email or imports them from IAM Identity Center, a user profile is automatically created. A user profile is the primary holder of settings for an individual user and has a reference to the user's private Amazon Elastic File System home directory.

Creates a user profile. A user profile represents a single user within a domain, and is the main way to reference a "person" for the purposes of sharing, reporting, and other user-oriented features. This entity is created when a user onboards to a domain. If an administrator invites a person by email or imports them from IAM Identity Center, a user profile is automatically created. A user profile is the primary holder of settings for an individual user and has a reference to the user's private Amazon Elastic File System home directory.

The VPC object you use to create or update a workforce.

A list of IP address ranges (CIDRs). Used to create an allow list of IP addresses for a private workforce. Workers will only be able to log in to their worker portal from an IP address within this range. By default, a workforce isn't restricted to specific IP addresses.

Use this parameter to configure your OIDC Identity Provider (IdP).

Use this operation to create a workforce. This operation will return an error if a workforce already exists in the Amazon Web Services Region that you specify. You can only create one workforce in each Amazon Web Services Region per Amazon Web Services account. If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use the DeleteWorkforce API operation to delete the existing workforce and then use CreateWorkforce to create a new workforce. To create a private workforce using Amazon Cognito, you must specify a Cognito user pool in CognitoConfig. You can also create an Amazon Cognito workforce using the Amazon SageMaker console. For more information, see Create a Private Workforce (Amazon Cognito). To create a private workforce using your own OIDC Identity Provider (IdP), specify your IdP configuration in OidcConfig. Your OIDC IdP must support groups because groups are used by Ground Truth and Amazon A2I to create work teams. For more information, see Create a Private Workforce (OIDC IdP).

Use this operation to create a workforce. This operation will return an error if a workforce already exists in the Amazon Web Services Region that you specify. You can only create one workforce in each Amazon Web Services Region per Amazon Web Services account. If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use the DeleteWorkforce API operation to delete the existing workforce and then use CreateWorkforce to create a new workforce. To create a private workforce using Amazon Cognito, you must specify a Cognito user pool in CognitoConfig. You can also create an Amazon Cognito workforce using the Amazon SageMaker console. For more information, see Create a Private Workforce (Amazon Cognito). To create a private workforce using your own OIDC Identity Provider (IdP), specify your IdP configuration in OidcConfig. Your OIDC IdP must support groups because groups are used by Ground Truth and Amazon A2I to create work teams. For more information, see Create a Private Workforce (OIDC IdP).

Use this parameter to specify a supported global condition key that is added to the IAM policy.

This object defines the access restrictions to Amazon S3 resources that are included in custom worker task templates using the Liquid filter, grant_read_access. To learn more about how custom templates are created, see Create custom worker task templates.

Use this optional parameter to constrain access to an Amazon S3 resource based on the IP address using supported IAM global condition keys. The Amazon S3 resource is accessed in the worker portal using a Amazon S3 presigned URL.

Configures Amazon SNS notifications of available or expiring work items for work teams.

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

Defines an Amazon Cognito or your own OIDC IdP user group that is part of a work team.

Creates a new work team for labeling your data. A work team is defined by one or more Amazon Cognito user pools. You must first create the user pools before you can create a work team. You cannot create more than 25 work teams in an account and region.

Creates a new work team for labeling your data. A work team is defined by one or more Amazon Cognito user pools. You must first create the user pools before you can create a work team. You cannot create more than 25 work teams in an account and region.

Sourcemodule CustomizedMetricSpecification = Awso_sagemaker.Values.CustomizedMetricSpecification

A customized metric.

The currently active data capture configuration used by your Endpoint.

Information about the status of the rule evaluation.

Sourcemodule DebugRuleEvaluationStatuses = Awso_sagemaker.Values.DebugRuleEvaluationStatuses
Sourcemodule InstanceGroupHealthCheckConfiguration = Awso_sagemaker.Values.InstanceGroupHealthCheckConfiguration

The configuration of deep health checks for an instance group. Overlapping deep health check configurations will be merged into a single operation.

Sourcemodule DeepHealthCheckConfigurations = Awso_sagemaker.Values.DeepHealthCheckConfigurations
Sourcemodule DeleteAIBenchmarkJobRequest = Awso_sagemaker.Values.DeleteAIBenchmarkJobRequest

Deletes the specified AI benchmark job.

Sourcemodule DeleteAIBenchmarkJobResponse = Awso_sagemaker.Values.DeleteAIBenchmarkJobResponse

Deletes the specified AI benchmark job.

Sourcemodule DeleteAIRecommendationJobRequest = Awso_sagemaker.Values.DeleteAIRecommendationJobRequest

Deletes the specified AI recommendation job.

Sourcemodule DeleteAIRecommendationJobResponse = Awso_sagemaker.Values.DeleteAIRecommendationJobResponse

Deletes the specified AI recommendation job.

Sourcemodule DeleteAIWorkloadConfigRequest = Awso_sagemaker.Values.DeleteAIWorkloadConfigRequest

Deletes the specified AI workload configuration. You cannot delete a configuration that is referenced by an active benchmark job.

Sourcemodule DeleteAIWorkloadConfigResponse = Awso_sagemaker.Values.DeleteAIWorkloadConfigResponse

Deletes the specified AI workload configuration. You cannot delete a configuration that is referenced by an active benchmark job.

Deletes an action.

Deletes an action.

Removes the specified algorithm from your account.

Sourcemodule DeleteAppImageConfigRequest = Awso_sagemaker.Values.DeleteAppImageConfigRequest

Deletes an AppImageConfig.

Used to stop and delete an app.

Deletes an artifact. Either ArtifactArn or Source must be specified.

Deletes an artifact. Either ArtifactArn or Source must be specified.

Deletes an association.

Deletes an association.

Delete a SageMaker HyperPod cluster.

Delete a SageMaker HyperPod cluster.

Sourcemodule DeleteClusterSchedulerConfigRequest = Awso_sagemaker.Values.DeleteClusterSchedulerConfigRequest

Deletes the cluster policy of the cluster.

Deletes the specified Git repository from your account.

Sourcemodule DeleteCompilationJobRequest = Awso_sagemaker.Values.DeleteCompilationJobRequest

Deletes the specified compilation job. This action deletes only the compilation job resource in Amazon SageMaker AI. It doesn't delete other resources that are related to that job, such as the model artifacts that the job creates, the compilation logs in CloudWatch, the compiled model, or the IAM role. You can delete a compilation job only if its current status is COMPLETED, FAILED, or STOPPED. If the job status is STARTING or INPROGRESS, stop the job, and then delete it after its status becomes STOPPED.

Deletes the compute allocation from the cluster.

Deletes an context.

Deletes an context.

Sourcemodule DeleteDataQualityJobDefinitionRequest = Awso_sagemaker.Values.DeleteDataQualityJobDefinitionRequest

Deletes a data quality monitoring job definition.

Deletes a fleet.

The retention policy for data stored on an Amazon Elastic File System volume.

Used to delete a domain. If you onboarded with IAM mode, you will need to delete your domain to onboard again using IAM Identity Center. Use with caution. All of the members of the domain will lose access to their EFS volume, including data, notebooks, and other artifacts.

Sourcemodule DeleteEdgeDeploymentPlanRequest = Awso_sagemaker.Values.DeleteEdgeDeploymentPlanRequest

Deletes an edge deployment plan if (and only if) all the stages in the plan are inactive or there are no stages in the plan.

Sourcemodule DeleteEdgeDeploymentStageRequest = Awso_sagemaker.Values.DeleteEdgeDeploymentStageRequest

Delete a stage in an edge deployment plan if (and only if) the stage is inactive.

Deletes an endpoint configuration. The DeleteEndpointConfig API deletes only the specified configuration. It does not delete endpoints created using the configuration. You must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. If you delete the EndpointConfig of an endpoint that is active or being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges.

Deletes an endpoint. SageMaker frees up all of the resources that were deployed when the endpoint was created. SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the RevokeGrant API call. When you delete your endpoint, SageMaker asynchronously deletes associated endpoint resources such as KMS key grants. You might still see these resources in your account for a few minutes after deleting your endpoint. Do not delete or revoke the permissions for your ExecutionRoleArn , otherwise SageMaker cannot delete these resources.

Deletes an SageMaker experiment. All trials associated with the experiment must be deleted first. Use the ListTrials API to get a list of the trials associated with the experiment.

Deletes an SageMaker experiment. All trials associated with the experiment must be deleted first. Use the ListTrials API to get a list of the trials associated with the experiment.

Delete the FeatureGroup and any data that was written to the OnlineStore of the FeatureGroup. Data cannot be accessed from the OnlineStore immediately after DeleteFeatureGroup is called. Data written into the OfflineStore will not be deleted. The Amazon Web Services Glue database and tables that are automatically created for your OfflineStore are not deleted. Note that it can take approximately 10-15 minutes to delete an OnlineStore FeatureGroup with the InMemory StorageType.

Sourcemodule DeleteFlowDefinitionRequest = Awso_sagemaker.Values.DeleteFlowDefinitionRequest

Deletes the specified flow definition.

Sourcemodule DeleteFlowDefinitionResponse = Awso_sagemaker.Values.DeleteFlowDefinitionResponse

Deletes the specified flow definition.

Sourcemodule DeleteHubContentReferenceRequest = Awso_sagemaker.Values.DeleteHubContentReferenceRequest

Delete a hub content reference in order to remove a model from a private hub.

Delete the contents of a hub.

Delete a hub.

Use this operation to delete a human task user interface (worker task template). To see a list of human task user interfaces (work task templates) in your account, use ListHumanTaskUis. When you delete a worker task template, it no longer appears when you call ListHumanTaskUis.

Use this operation to delete a human task user interface (worker task template). To see a list of human task user interfaces (work task templates) in your account, use ListHumanTaskUis. When you delete a worker task template, it no longer appears when you call ListHumanTaskUis.

Sourcemodule DeleteHyperParameterTuningJobRequest = Awso_sagemaker.Values.DeleteHyperParameterTuningJobRequest

Deletes a hyperparameter tuning job. The DeleteHyperParameterTuningJob API deletes only the tuning job entry that was created in SageMaker when you called the CreateHyperParameterTuningJob API. It does not delete training jobs, artifacts, or the IAM role that you specified when creating the model.

Deletes a SageMaker AI image and all versions of the image. The container images aren't deleted.

Deletes a SageMaker AI image and all versions of the image. The container images aren't deleted.

Deletes a version of a SageMaker AI image. The container image the version represents isn't deleted.

Sourcemodule DeleteImageVersionResponse = Awso_sagemaker.Values.DeleteImageVersionResponse

Deletes a version of a SageMaker AI image. The container image the version represents isn't deleted.

Sourcemodule DeleteInferenceComponentInput = Awso_sagemaker.Values.DeleteInferenceComponentInput

Deletes an inference component.

Sourcemodule DeleteInferenceExperimentRequest = Awso_sagemaker.Values.DeleteInferenceExperimentRequest

Deletes an inference experiment. This operation does not delete your endpoint, variants, or any underlying resources. This operation only deletes the metadata of your experiment.

Sourcemodule DeleteInferenceExperimentResponse = Awso_sagemaker.Values.DeleteInferenceExperimentResponse

Deletes an inference experiment. This operation does not delete your endpoint, variants, or any underlying resources. This operation only deletes the metadata of your experiment.

Deletes an MLflow App.

Deletes an MLflow App.

Sourcemodule DeleteMlflowTrackingServerRequest = Awso_sagemaker.Values.DeleteMlflowTrackingServerRequest

Deletes an MLflow Tracking Server. For more information, see Clean up MLflow resources.

Sourcemodule DeleteMlflowTrackingServerResponse = Awso_sagemaker.Values.DeleteMlflowTrackingServerResponse

Deletes an MLflow Tracking Server. For more information, see Clean up MLflow resources.

Sourcemodule DeleteModelBiasJobDefinitionRequest = Awso_sagemaker.Values.DeleteModelBiasJobDefinitionRequest

Deletes an Amazon SageMaker AI model bias job definition.

Deletes an Amazon SageMaker Model Card.

Sourcemodule DeleteModelExplainabilityJobDefinitionRequest = Awso_sagemaker.Values.DeleteModelExplainabilityJobDefinitionRequest

Deletes an Amazon SageMaker AI model explainability job definition.

Deletes a model. The DeleteModel API deletes only the model entry that was created in SageMaker when you called the CreateModel API. It does not delete model artifacts, inference code, or the IAM role that you specified when creating the model.

Sourcemodule DeleteModelPackageGroupInput = Awso_sagemaker.Values.DeleteModelPackageGroupInput

Deletes the specified model group.

Sourcemodule DeleteModelPackageGroupPolicyInput = Awso_sagemaker.Values.DeleteModelPackageGroupPolicyInput

Deletes a model group resource policy.

Deletes a model package. A model package is used to create SageMaker models or list on Amazon Web Services Marketplace. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.

Sourcemodule DeleteModelQualityJobDefinitionRequest = Awso_sagemaker.Values.DeleteModelQualityJobDefinitionRequest

Deletes the secified model quality monitoring job definition.

Sourcemodule DeleteMonitoringScheduleRequest = Awso_sagemaker.Values.DeleteMonitoringScheduleRequest

Deletes a monitoring schedule. Also stops the schedule had not already been stopped. This does not delete the job execution history of the monitoring schedule.

Sourcemodule DeleteNotebookInstanceInput = Awso_sagemaker.Values.DeleteNotebookInstanceInput

Deletes an SageMaker AI notebook instance. Before you can delete a notebook instance, you must call the StopNotebookInstance API. When you delete a notebook instance, you lose all of your data. SageMaker AI removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.

Sourcemodule DeleteNotebookInstanceLifecycleConfigInput = Awso_sagemaker.Values.DeleteNotebookInstanceLifecycleConfigInput

Deletes a notebook instance lifecycle configuration.

Sourcemodule DeleteOptimizationJobRequest = Awso_sagemaker.Values.DeleteOptimizationJobRequest

Deletes an optimization job.

Deletes a SageMaker Partner AI App.

Deletes a SageMaker Partner AI App.

Deletes a pipeline if there are no running instances of the pipeline. To delete a pipeline, you must stop all running instances of the pipeline using the StopPipelineExecution API. When you delete a pipeline, all instances of the pipeline are deleted.

Deletes a pipeline if there are no running instances of the pipeline. To delete a pipeline, you must stop all running instances of the pipeline using the StopPipelineExecution API. When you delete a pipeline, all instances of the pipeline are deleted.

Sourcemodule DeleteProcessingJobRequest = Awso_sagemaker.Values.DeleteProcessingJobRequest

Deletes a processing job. After Amazon SageMaker deletes a processing job, all of the metadata for the processing job is lost. You can delete only processing jobs that are in a terminal state (Stopped, Failed, or Completed). You cannot delete a job that is in the InProgress or Stopping state. After deleting the job, you can reuse its name to create another processing job.

Delete the specified project.

Used to delete a space.

Sourcemodule DeleteStudioLifecycleConfigRequest = Awso_sagemaker.Values.DeleteStudioLifecycleConfigRequest

Deletes the Amazon SageMaker AI Studio Lifecycle Configuration. In order to delete the Lifecycle Configuration, there must be no running apps using the Lifecycle Configuration. You must also remove the Lifecycle Configuration from UserSettings in all Domains and UserProfiles.

Deletes the specified tags from an SageMaker resource. To list a resource's tags, use the ListTags API. When you call this API to delete tags from a hyperparameter tuning job, the deleted tags are not removed from training jobs that the hyperparameter tuning job launched before you called this API. When you call this API to delete tags from a SageMaker Domain or User Profile, the deleted tags are not removed from Apps that the SageMaker Domain or User Profile launched before you called this API.

Deletes the specified tags from an SageMaker resource. To list a resource's tags, use the ListTags API. When you call this API to delete tags from a hyperparameter tuning job, the deleted tags are not removed from training jobs that the hyperparameter tuning job launched before you called this API. When you call this API to delete tags from a SageMaker Domain or User Profile, the deleted tags are not removed from Apps that the SageMaker Domain or User Profile launched before you called this API.

Deletes a training job. After SageMaker deletes a training job, all of the metadata for the training job is lost. You can delete only training jobs that are in a terminal state (Stopped, Failed, or Completed) and don't retain an Available managed warm pool. You cannot delete a job that is in the InProgress or Stopping state. After deleting the job, you can reuse its name to create another training job.

Sourcemodule DeleteTrialComponentRequest = Awso_sagemaker.Values.DeleteTrialComponentRequest

Deletes the specified trial component. A trial component must be disassociated from all trials before the trial component can be deleted. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.

Sourcemodule DeleteTrialComponentResponse = Awso_sagemaker.Values.DeleteTrialComponentResponse

Deletes the specified trial component. A trial component must be disassociated from all trials before the trial component can be deleted. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.

Deletes the specified trial. All trial components that make up the trial must be deleted first. Use the DescribeTrialComponent API to get the list of trial components.

Deletes the specified trial. All trial components that make up the trial must be deleted first. Use the DescribeTrialComponent API to get the list of trial components.

Deletes a user profile. When a user profile is deleted, the user loses access to their EFS volume, including data, notebooks, and other artifacts.

Use this operation to delete a workforce. If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use this operation to delete the existing workforce and then use CreateWorkforce to create a new workforce. If a private workforce contains one or more work teams, you must use the DeleteWorkteam operation to delete all work teams before you delete the workforce. If you try to delete a workforce that contains one or more work teams, you will receive a ResourceInUse error.

Use this operation to delete a workforce. If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use this operation to delete the existing workforce and then use CreateWorkforce to create a new workforce. If a private workforce contains one or more work teams, you must use the DeleteWorkteam operation to delete all work teams before you delete the workforce. If you try to delete a workforce that contains one or more work teams, you will receive a ResourceInUse error.

Deletes an existing work team. This operation can't be undone.

Deletes an existing work team. This operation can't be undone.

Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant. If you used the registry/repository[:tag] form to specify the image path of the primary container when you created the model hosted in this ProductionVariant, the path resolves to a path of the form registry/repository[@digest]. A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide.

Sourcemodule RealTimeInferenceRecommendation = Awso_sagemaker.Values.RealTimeInferenceRecommendation

The recommended configuration to use for Real-Time Inference.

Sourcemodule RealTimeInferenceRecommendations = Awso_sagemaker.Values.RealTimeInferenceRecommendations

A set of recommended deployment configurations for the model. To get more advanced recommendations, see CreateInferenceRecommendationsJob to create an inference recommendation job.

Contains information summarizing the deployment stage results.

Sourcemodule DeploymentStageStatusSummary = Awso_sagemaker.Values.DeploymentStageStatusSummary

Contains information summarizing the deployment stage results.

Sourcemodule DeploymentStageStatusSummaries = Awso_sagemaker.Values.DeploymentStageStatusSummaries

Deregisters the specified devices. After you deregister a device, you will need to re-register the devices.

Information that SageMaker Neo automatically derived about the model.

Sourcemodule DescribeAIBenchmarkJobRequest = Awso_sagemaker.Values.DescribeAIBenchmarkJobRequest

Returns details of an AI benchmark job, including its status, configuration, target endpoint, and timing information.

Sourcemodule DescribeAIBenchmarkJobResponse = Awso_sagemaker.Values.DescribeAIBenchmarkJobResponse

Returns details of an AI benchmark job, including its status, configuration, target endpoint, and timing information.

Sourcemodule DescribeAIRecommendationJobRequest = Awso_sagemaker.Values.DescribeAIRecommendationJobRequest

Returns details of an AI recommendation job, including its status, model source, performance targets, optimization recommendations, and deployment configurations.

Sourcemodule DescribeAIRecommendationJobResponse = Awso_sagemaker.Values.DescribeAIRecommendationJobResponse

Returns details of an AI recommendation job, including its status, model source, performance targets, optimization recommendations, and deployment configurations.

Sourcemodule DescribeAIWorkloadConfigRequest = Awso_sagemaker.Values.DescribeAIWorkloadConfigRequest

Returns details of an AI workload configuration, including the dataset configuration, benchmark tool settings, tags, and creation time.

Sourcemodule DescribeAIWorkloadConfigResponse = Awso_sagemaker.Values.DescribeAIWorkloadConfigResponse

Returns details of an AI workload configuration, including the dataset configuration, benchmark tool settings, tags, and creation time.

Describes an action.

Describes an action.

Returns a description of the specified algorithm that is in your account.

Returns a description of the specified algorithm that is in your account.

Sourcemodule DescribeAppImageConfigRequest = Awso_sagemaker.Values.DescribeAppImageConfigRequest

Describes an AppImageConfig.

Sourcemodule DescribeAppImageConfigResponse = Awso_sagemaker.Values.DescribeAppImageConfigResponse

Describes an AppImageConfig.

Describes the app.

Describes the app.

Describes an artifact.

Describes an artifact.

Returns information about an AutoML job created by calling CreateAutoMLJob. AutoML jobs created by calling CreateAutoMLJobV2 cannot be described by DescribeAutoMLJob.

The resolved attributes.

Provides information about the endpoint of the model deployment.

Returns information about an AutoML job created by calling CreateAutoMLJob. AutoML jobs created by calling CreateAutoMLJobV2 cannot be described by DescribeAutoMLJob.

Sourcemodule DescribeAutoMLJobV2Request = Awso_sagemaker.Values.DescribeAutoMLJobV2Request

Returns information about an AutoML job created by calling CreateAutoMLJobV2 or CreateAutoMLJob.

Sourcemodule DescribeAutoMLJobV2Response = Awso_sagemaker.Values.DescribeAutoMLJobV2Response

Returns information about an AutoML job created by calling CreateAutoMLJobV2 or CreateAutoMLJob.

Sourcemodule DescribeClusterEventRequest = Awso_sagemaker.Values.DescribeClusterEventRequest

Retrieves detailed information about a specific event for a given HyperPod cluster. This functionality is only supported when the NodeProvisioningMode is set to Continuous.

Sourcemodule DescribeClusterEventResponse = Awso_sagemaker.Values.DescribeClusterEventResponse

Retrieves detailed information about a specific event for a given HyperPod cluster. This functionality is only supported when the NodeProvisioningMode is set to Continuous.

Sourcemodule DescribeClusterNodeRequest = Awso_sagemaker.Values.DescribeClusterNodeRequest

Retrieves information of a node (also called a instance interchangeably) of a SageMaker HyperPod cluster.

Sourcemodule DescribeClusterNodeResponse = Awso_sagemaker.Values.DescribeClusterNodeResponse

Retrieves information of a node (also called a instance interchangeably) of a SageMaker HyperPod cluster.

Retrieves information of a SageMaker HyperPod cluster.

Retrieves information of a SageMaker HyperPod cluster.

Sourcemodule DescribeClusterSchedulerConfigRequest = Awso_sagemaker.Values.DescribeClusterSchedulerConfigRequest

Description of the cluster policy. This policy is used for task prioritization and fair-share allocation. This helps prioritize critical workloads and distributes idle compute across entities.

Sourcemodule DescribeClusterSchedulerConfigResponse = Awso_sagemaker.Values.DescribeClusterSchedulerConfigResponse

Description of the cluster policy. This policy is used for task prioritization and fair-share allocation. This helps prioritize critical workloads and distributes idle compute across entities.

Sourcemodule DescribeCodeRepositoryInput = Awso_sagemaker.Values.DescribeCodeRepositoryInput

Gets details about the specified Git repository.

Sourcemodule DescribeCodeRepositoryOutput = Awso_sagemaker.Values.DescribeCodeRepositoryOutput

Gets details about the specified Git repository.

Sourcemodule DescribeCompilationJobRequest = Awso_sagemaker.Values.DescribeCompilationJobRequest

Returns information about a model compilation job. To create a model compilation job, use CreateCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.

Provides information to verify the integrity of stored model artifacts.

Provides information about the location that is configured for storing model artifacts. Model artifacts are outputs that result from training a model. They typically consist of trained parameters, a model definition that describes how to compute inferences, and other metadata. A SageMaker container stores your trained model artifacts in the /opt/ml/model directory. After training has completed, by default, these artifacts are uploaded to your Amazon S3 bucket as compressed files.

Sourcemodule DescribeCompilationJobResponse = Awso_sagemaker.Values.DescribeCompilationJobResponse

Returns information about a model compilation job. To create a model compilation job, use CreateCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.

Sourcemodule DescribeComputeQuotaRequest = Awso_sagemaker.Values.DescribeComputeQuotaRequest

Description of the compute allocation definition.

Sourcemodule DescribeComputeQuotaResponse = Awso_sagemaker.Values.DescribeComputeQuotaResponse

Description of the compute allocation definition.

Describes a context.

Describes a context.

Sourcemodule DescribeDataQualityJobDefinitionRequest = Awso_sagemaker.Values.DescribeDataQualityJobDefinitionRequest

Gets the details of a data quality monitoring job definition.

Sourcemodule DescribeDataQualityJobDefinitionResponse = Awso_sagemaker.Values.DescribeDataQualityJobDefinitionResponse

Gets the details of a data quality monitoring job definition.

Sourcemodule DescribeDeviceFleetRequest = Awso_sagemaker.Values.DescribeDeviceFleetRequest

A description of the fleet the device belongs to.

Sourcemodule DescribeDeviceFleetResponse = Awso_sagemaker.Values.DescribeDeviceFleetResponse

A description of the fleet the device belongs to.

Describes the device.

The model on the edge device.

Describes the device.

The description of the domain.

The description of the domain.

Sourcemodule DescribeEdgeDeploymentPlanRequest = Awso_sagemaker.Values.DescribeEdgeDeploymentPlanRequest

Describes an edge deployment plan with deployment status per stage.

Sourcemodule DescribeEdgeDeploymentPlanResponse = Awso_sagemaker.Values.DescribeEdgeDeploymentPlanResponse

Describes an edge deployment plan with deployment status per stage.

Sourcemodule DescribeEdgePackagingJobRequest = Awso_sagemaker.Values.DescribeEdgePackagingJobRequest

A description of edge packaging jobs.

Sourcemodule EdgePresetDeploymentStatus = Awso_sagemaker.Values.EdgePresetDeploymentStatus
Sourcemodule EdgePresetDeploymentArtifact = Awso_sagemaker.Values.EdgePresetDeploymentArtifact
Sourcemodule EdgePresetDeploymentOutput = Awso_sagemaker.Values.EdgePresetDeploymentOutput

The output of a SageMaker Edge Manager deployable resource.

Sourcemodule DescribeEdgePackagingJobResponse = Awso_sagemaker.Values.DescribeEdgePackagingJobResponse

A description of edge packaging jobs.

Sourcemodule DescribeEndpointConfigInput = Awso_sagemaker.Values.DescribeEndpointConfigInput

Returns the description of an endpoint configuration created using the CreateEndpointConfig API.

Sourcemodule DescribeEndpointConfigOutput = Awso_sagemaker.Values.DescribeEndpointConfigOutput

Returns the description of an endpoint configuration created using the CreateEndpointConfig API.

Returns the description of an endpoint.

Describes the status of the production variant.

Sourcemodule ProductionVariantStatusList = Awso_sagemaker.Values.ProductionVariantStatusList

The EC2 capacity reservations that are shared to an ML capacity reservation.

Sourcemodule Ec2CapacityReservationsList = Awso_sagemaker.Values.Ec2CapacityReservationsList
Sourcemodule ProductionVariantCapacityReservationSummary = Awso_sagemaker.Values.ProductionVariantCapacityReservationSummary

Details about an ML capacity reservation.

A summary of an instance pool for a production variant, including the instance type and the current number of instances.

Describes weight and capacities for a production variant associated with an endpoint. If you sent a request to the UpdateEndpointWeightsAndCapacities API and the endpoint status is Updating, you get different desired and current values.

Sourcemodule ProductionVariantSummaryList = Awso_sagemaker.Values.ProductionVariantSummaryList
Sourcemodule PendingProductionVariantSummary = Awso_sagemaker.Values.PendingProductionVariantSummary

The production variant summary for a deployment when an endpoint is creating or updating with the CreateEndpoint or UpdateEndpoint operations. Describes the VariantStatus , weight and capacity for a production variant associated with an endpoint.

Sourcemodule PendingProductionVariantSummaryList = Awso_sagemaker.Values.PendingProductionVariantSummaryList

The summary of an in-progress deployment when an endpoint is creating or updating with a new endpoint configuration.

Returns the description of an endpoint.

Provides a list of an experiment's properties.

The source of the experiment.

Sourcemodule DescribeExperimentResponse = Awso_sagemaker.Values.DescribeExperimentResponse

Provides a list of an experiment's properties.

Sourcemodule DescribeFeatureGroupRequest = Awso_sagemaker.Values.DescribeFeatureGroupRequest

Use this operation to describe a FeatureGroup. The response includes information on the creation time, FeatureGroup name, the unique identifier for each FeatureGroup, and more.

Sourcemodule ThroughputConfigDescription = Awso_sagemaker.Values.ThroughputConfigDescription

Active throughput configuration of the feature group. There are two modes: ON_DEMAND and PROVISIONED. With on-demand mode, you are charged for data reads and writes that your application performs on your feature group. You do not need to specify read and write throughput because Feature Store accommodates your workloads as they ramp up and down. You can switch a feature group to on-demand only once in a 24 hour period. With provisioned throughput mode, you specify the read and write capacity per second that you expect your application to require, and you are billed based on those limits. Exceeding provisioned throughput will result in your requests being throttled. Note: PROVISIONED throughput mode is supported only for feature groups that are offline-only, or use the Standard tier online store.

The status of OfflineStore.

A value that indicates whether the update was successful.

Sourcemodule DescribeFeatureGroupResponse = Awso_sagemaker.Values.DescribeFeatureGroupResponse

Use this operation to describe a FeatureGroup. The response includes information on the creation time, FeatureGroup name, the unique identifier for each FeatureGroup, and more.

Sourcemodule DescribeFeatureMetadataRequest = Awso_sagemaker.Values.DescribeFeatureMetadataRequest

Shows the metadata for a feature within a feature group.

A key-value pair that you specify to describe the feature.

Sourcemodule DescribeFeatureMetadataResponse = Awso_sagemaker.Values.DescribeFeatureMetadataResponse

Shows the metadata for a feature within a feature group.

Sourcemodule DescribeFlowDefinitionRequest = Awso_sagemaker.Values.DescribeFlowDefinitionRequest

Returns information about the specified flow definition.

Sourcemodule DescribeFlowDefinitionResponse = Awso_sagemaker.Values.DescribeFlowDefinitionResponse

Returns information about the specified flow definition.

Describe the content of a hub.

Sourcemodule HubContentSearchKeywordList = Awso_sagemaker.Values.HubContentSearchKeywordList

Any dependencies related to hub content, such as scripts, model artifacts, datasets, or notebooks.

Sourcemodule DescribeHubContentResponse = Awso_sagemaker.Values.DescribeHubContentResponse

Describe the content of a hub.

Describes a hub.

Describes a hub.

Sourcemodule DescribeHumanTaskUiRequest = Awso_sagemaker.Values.DescribeHumanTaskUiRequest

Returns information about the requested human task user interface (worker task template).

Container for user interface template information.

Sourcemodule DescribeHumanTaskUiResponse = Awso_sagemaker.Values.DescribeHumanTaskUiResponse

Returns information about the requested human task user interface (worker task template).

Sourcemodule DescribeHyperParameterTuningJobRequest = Awso_sagemaker.Values.DescribeHyperParameterTuningJobRequest

Returns a description of a hyperparameter tuning job, depending on the fields selected. These fields can include the name, Amazon Resource Name (ARN), job status of your tuning job and more.

The numbers of training jobs launched by a hyperparameter tuning job, categorized by status.

Specifies the number of training jobs that this hyperparameter tuning job launched, categorized by the status of their objective metric. The objective metric status shows whether the final objective metric for the training job has been evaluated by the tuning job and used in the hyperparameter tuning process.

Sourcemodule HyperParameterTuningJobStatus = Awso_sagemaker.Values.HyperParameterTuningJobStatus
Sourcemodule HyperParameterTuningJobConsumedResources = Awso_sagemaker.Values.HyperParameterTuningJobConsumedResources

The total resources consumed by your hyperparameter tuning job.

Sourcemodule HyperParameterTuningJobCompletionDetails = Awso_sagemaker.Values.HyperParameterTuningJobCompletionDetails

A structure that contains runtime information about both current and completed hyperparameter tuning jobs.

Sourcemodule FinalHyperParameterTuningJobObjectiveMetric = Awso_sagemaker.Values.FinalHyperParameterTuningJobObjectiveMetric

Shows the latest objective metric emitted by a training job that was launched by a hyperparameter tuning job. You define the objective metric in the HyperParameterTuningJobObjective parameter of HyperParameterTuningJobConfig.

Sourcemodule HyperParameterTrainingJobSummary = Awso_sagemaker.Values.HyperParameterTrainingJobSummary

The container for the summary information about a training job.

Sourcemodule DescribeHyperParameterTuningJobResponse = Awso_sagemaker.Values.DescribeHyperParameterTuningJobResponse

Returns a description of a hyperparameter tuning job, depending on the fields selected. These fields can include the name, Amazon Resource Name (ARN), job status of your tuning job and more.

Describes a SageMaker AI image.

Describes a SageMaker AI image.

Sourcemodule DescribeImageVersionRequest = Awso_sagemaker.Values.DescribeImageVersionRequest

Describes a version of a SageMaker AI image.

Sourcemodule DescribeImageVersionResponse = Awso_sagemaker.Values.DescribeImageVersionResponse

Describes a version of a SageMaker AI image.

Sourcemodule DescribeInferenceComponentInput = Awso_sagemaker.Values.DescribeInferenceComponentInput

Returns information about an inference component.

Sourcemodule InferenceComponentDataCacheConfigSummary = Awso_sagemaker.Values.InferenceComponentDataCacheConfigSummary

Settings that affect how the inference component caches data.

Sourcemodule InferenceComponentContainerSpecificationSummary = Awso_sagemaker.Values.InferenceComponentContainerSpecificationSummary

Details about the resources that are deployed with this inference component.

Sourcemodule InferenceComponentSpecificationSummary = Awso_sagemaker.Values.InferenceComponentSpecificationSummary

Details about the resources that are deployed with this inference component.

Sourcemodule InferenceComponentSpecificationSummaryList = Awso_sagemaker.Values.InferenceComponentSpecificationSummaryList
Sourcemodule InferenceComponentPlacementStatus = Awso_sagemaker.Values.InferenceComponentPlacementStatus

The placement status of an inference component on a specific instance type. Shows the number of inference component copies currently placed on instances of a given type.

Sourcemodule InferenceComponentPlacementStatusList = Awso_sagemaker.Values.InferenceComponentPlacementStatusList
Sourcemodule InferenceComponentRuntimeConfigSummary = Awso_sagemaker.Values.InferenceComponentRuntimeConfigSummary

Details about the runtime settings for the model that is deployed with the inference component.

Sourcemodule InferenceComponentCapacitySizeType = Awso_sagemaker.Values.InferenceComponentCapacitySizeType
Sourcemodule InferenceComponentCapacitySize = Awso_sagemaker.Values.InferenceComponentCapacitySize

Specifies the type and size of the endpoint capacity to activate for a rolling deployment or a rollback strategy. You can specify your batches as either of the following: A count of inference component copies The overall percentage or your fleet For a rollback strategy, if you don't specify the fields in this object, or if you set the Value parameter to 100%, then SageMaker AI uses a blue/green rollback strategy and rolls all traffic back to the blue fleet.

Sourcemodule InferenceComponentRollingUpdatePolicy = Awso_sagemaker.Values.InferenceComponentRollingUpdatePolicy

Specifies a rolling deployment strategy for updating a SageMaker AI inference component.

Sourcemodule InferenceComponentDeploymentConfig = Awso_sagemaker.Values.InferenceComponentDeploymentConfig

The deployment configuration for an endpoint that hosts inference components. The configuration includes the desired deployment strategy and rollback settings.

Sourcemodule DescribeInferenceComponentOutput = Awso_sagemaker.Values.DescribeInferenceComponentOutput

Returns information about an inference component.

Sourcemodule DescribeInferenceExperimentRequest = Awso_sagemaker.Values.DescribeInferenceExperimentRequest

Returns details about an inference experiment.

Summary of the deployment configuration of a model.

Sourcemodule ModelVariantConfigSummaryList = Awso_sagemaker.Values.ModelVariantConfigSummaryList
Sourcemodule InferenceExperimentStatusReason = Awso_sagemaker.Values.InferenceExperimentStatusReason

The metadata of the endpoint.

Sourcemodule DescribeInferenceExperimentResponse = Awso_sagemaker.Values.DescribeInferenceExperimentResponse

Returns details about an inference experiment.

Sourcemodule DescribeInferenceRecommendationsJobRequest = Awso_sagemaker.Values.DescribeInferenceRecommendationsJobRequest

Provides the results of the Inference Recommender job. One or more recommendation jobs are returned.

The metrics of recommendations.

Sourcemodule RecommendationJobCompilationJobName = Awso_sagemaker.Values.RecommendationJobCompilationJobName

A list of environment parameters suggested by the Amazon SageMaker Inference Recommender.

Defines the model configuration. Includes the specification name and environment parameters.

Sourcemodule EndpointOutputConfiguration = Awso_sagemaker.Values.EndpointOutputConfiguration

The endpoint configuration made by Inference Recommender during a recommendation job.

A list of recommendations made by Amazon SageMaker Inference Recommender.

The metrics for an existing endpoint compared in an Inference Recommender job.

The performance results from running an Inference Recommender job on an existing endpoint.

Sourcemodule DescribeInferenceRecommendationsJobResponse = Awso_sagemaker.Values.DescribeInferenceRecommendationsJobResponse

Provides the results of the Inference Recommender job. One or more recommendation jobs are returned.

Sourcemodule DescribeLabelingJobRequest = Awso_sagemaker.Values.DescribeLabelingJobRequest

Gets information about a labeling job.

Specifies the location of the output produced by the labeling job.

Provides a breakdown of the number of objects labeled.

Sourcemodule DescribeLabelingJobResponse = Awso_sagemaker.Values.DescribeLabelingJobResponse

Gets information about a labeling job.

Sourcemodule DescribeLineageGroupRequest = Awso_sagemaker.Values.DescribeLineageGroupRequest

Provides a list of properties for the requested lineage group. For more information, see Cross-Account Lineage Tracking in the Amazon SageMaker Developer Guide.

Sourcemodule DescribeLineageGroupResponse = Awso_sagemaker.Values.DescribeLineageGroupResponse

Provides a list of properties for the requested lineage group. For more information, see Cross-Account Lineage Tracking in the Amazon SageMaker Developer Guide.

Returns information about an MLflow App.

Returns information about an MLflow App.

Sourcemodule DescribeMlflowTrackingServerRequest = Awso_sagemaker.Values.DescribeMlflowTrackingServerRequest

Returns information about an MLflow Tracking Server.

Sourcemodule TrackingServerMaintenanceStatus = Awso_sagemaker.Values.TrackingServerMaintenanceStatus
Sourcemodule DescribeMlflowTrackingServerResponse = Awso_sagemaker.Values.DescribeMlflowTrackingServerResponse

Returns information about an MLflow Tracking Server.

Sourcemodule DescribeModelBiasJobDefinitionRequest = Awso_sagemaker.Values.DescribeModelBiasJobDefinitionRequest

Returns a description of a model bias job definition.

Sourcemodule DescribeModelBiasJobDefinitionResponse = Awso_sagemaker.Values.DescribeModelBiasJobDefinitionResponse

Returns a description of a model bias job definition.

Sourcemodule DescribeModelCardExportJobRequest = Awso_sagemaker.Values.DescribeModelCardExportJobRequest

Describes an Amazon SageMaker Model Card export job.

The artifacts of the model card export job.

Sourcemodule DescribeModelCardExportJobResponse = Awso_sagemaker.Values.DescribeModelCardExportJobResponse

Describes an Amazon SageMaker Model Card export job.

Describes the content, creation time, and security configuration of an Amazon SageMaker Model Card.

Describes the content, creation time, and security configuration of an Amazon SageMaker Model Card.

Sourcemodule DescribeModelExplainabilityJobDefinitionRequest = Awso_sagemaker.Values.DescribeModelExplainabilityJobDefinitionRequest

Returns a description of a model explainability job definition.

Sourcemodule DescribeModelExplainabilityJobDefinitionResponse = Awso_sagemaker.Values.DescribeModelExplainabilityJobDefinitionResponse

Returns a description of a model explainability job definition.

Describes a model that you created using the CreateModel API.

Describes a model that you created using the CreateModel API.

Sourcemodule DescribeModelPackageGroupInput = Awso_sagemaker.Values.DescribeModelPackageGroupInput

Gets a description for the specified model group.

Sourcemodule DescribeModelPackageGroupOutput = Awso_sagemaker.Values.DescribeModelPackageGroupOutput

Gets a description for the specified model group.

Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace. If you provided a KMS Key ID when you created your model package, you will see the KMS Decrypt API call in your CloudTrail logs when you use this API. To create models in SageMaker, buyers can subscribe to model packages listed on Amazon Web Services Marketplace.

Sourcemodule DetailedModelPackageStatus = Awso_sagemaker.Values.DetailedModelPackageStatus

Represents the overall status of a model package.

Sourcemodule ModelPackageStatusItemList = Awso_sagemaker.Values.ModelPackageStatusItemList

Specifies the validation and image scan statuses of the model package.

Sourcemodule DescribeModelPackageOutput = Awso_sagemaker.Values.DescribeModelPackageOutput

Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace. If you provided a KMS Key ID when you created your model package, you will see the KMS Decrypt API call in your CloudTrail logs when you use this API. To create models in SageMaker, buyers can subscribe to model packages listed on Amazon Web Services Marketplace.

Sourcemodule DescribeModelQualityJobDefinitionRequest = Awso_sagemaker.Values.DescribeModelQualityJobDefinitionRequest

Returns a description of a model quality job definition.

Sourcemodule DescribeModelQualityJobDefinitionResponse = Awso_sagemaker.Values.DescribeModelQualityJobDefinitionResponse

Returns a description of a model quality job definition.

Sourcemodule DescribeMonitoringScheduleRequest = Awso_sagemaker.Values.DescribeMonitoringScheduleRequest

Describes the schedule for a monitoring job.

Sourcemodule MonitoringExecutionSummary = Awso_sagemaker.Values.MonitoringExecutionSummary

Summary of information about the last monitoring job to run.

Sourcemodule DescribeMonitoringScheduleResponse = Awso_sagemaker.Values.DescribeMonitoringScheduleResponse

Describes the schedule for a monitoring job.

Sourcemodule DescribeNotebookInstanceInput = Awso_sagemaker.Values.DescribeNotebookInstanceInput

Returns information about a notebook instance.

Sourcemodule DescribeNotebookInstanceLifecycleConfigInput = Awso_sagemaker.Values.DescribeNotebookInstanceLifecycleConfigInput

Returns a description of a notebook instance lifecycle configuration. For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

Sourcemodule DescribeNotebookInstanceLifecycleConfigOutput = Awso_sagemaker.Values.DescribeNotebookInstanceLifecycleConfigOutput

Returns a description of a notebook instance lifecycle configuration. For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

Sourcemodule DescribeNotebookInstanceOutput = Awso_sagemaker.Values.DescribeNotebookInstanceOutput

Returns information about a notebook instance.

Sourcemodule DescribeOptimizationJobRequest = Awso_sagemaker.Values.DescribeOptimizationJobRequest

Provides the properties of the specified optimization job.

Output values produced by an optimization job.

Sourcemodule DescribeOptimizationJobResponse = Awso_sagemaker.Values.DescribeOptimizationJobResponse

Provides the properties of the specified optimization job.

Gets information about a SageMaker Partner AI App.

This is an error field object that contains the error code and the reason for an operation failure.

Sourcemodule DescribePartnerAppResponse = Awso_sagemaker.Values.DescribePartnerAppResponse

Gets information about a SageMaker Partner AI App.

Sourcemodule DescribePipelineDefinitionForExecutionRequest = Awso_sagemaker.Values.DescribePipelineDefinitionForExecutionRequest

Describes the details of an execution's pipeline definition.

Sourcemodule DescribePipelineDefinitionForExecutionResponse = Awso_sagemaker.Values.DescribePipelineDefinitionForExecutionResponse

Describes the details of an execution's pipeline definition.

Sourcemodule DescribePipelineExecutionRequest = Awso_sagemaker.Values.DescribePipelineExecutionRequest

Describes the details of a pipeline execution.

A step selected to run in selective execution mode.

The selective execution configuration applied to the pipeline run.

Specifies the names of the experiment and trial created by a pipeline.

Sourcemodule PipelineExecutionFailureReason = Awso_sagemaker.Values.PipelineExecutionFailureReason
Sourcemodule PipelineExecutionDescription = Awso_sagemaker.Values.PipelineExecutionDescription
Sourcemodule MlflowExperimentEntityName = Awso_sagemaker.Values.MlflowExperimentEntityName

The MLflow configuration.

Sourcemodule DescribePipelineExecutionResponse = Awso_sagemaker.Values.DescribePipelineExecutionResponse

Describes the details of a pipeline execution.

Describes the details of a pipeline.

Sourcemodule PipelineVersionDescription = Awso_sagemaker.Values.PipelineVersionDescription

Describes the details of a pipeline.

Sourcemodule DescribeProcessingJobRequest = Awso_sagemaker.Values.DescribeProcessingJobRequest

Returns a description of a processing job.

Sourcemodule DescribeProcessingJobResponse = Awso_sagemaker.Values.DescribeProcessingJobResponse

Returns a description of a processing job.

Describes the details of a project.

Details about a template provider configuration and associated provisioning information.

Sourcemodule TemplateProviderDetailList = Awso_sagemaker.Values.TemplateProviderDetailList
Sourcemodule ProvisionedProductStatusMessage = Awso_sagemaker.Values.ProvisionedProductStatusMessage
Sourcemodule ServiceCatalogProvisionedProductDetails = Awso_sagemaker.Values.ServiceCatalogProvisionedProductDetails

Details of a provisioned service catalog product. For information about service catalog, see What is Amazon Web Services Service Catalog.

Describes the details of a project.

Sourcemodule DescribeReservedCapacityRequest = Awso_sagemaker.Values.DescribeReservedCapacityRequest

Retrieves details about a reserved capacity.

Sourcemodule ReservedCapacityInstanceType = Awso_sagemaker.Values.ReservedCapacityInstanceType

A summary of UltraServer resources and their current status.

Sourcemodule ReservedCapacityDurationMinutes = Awso_sagemaker.Values.ReservedCapacityDurationMinutes
Sourcemodule ReservedCapacityDurationHours = Awso_sagemaker.Values.ReservedCapacityDurationHours
Sourcemodule DescribeReservedCapacityResponse = Awso_sagemaker.Values.DescribeReservedCapacityResponse

Retrieves details about a reserved capacity.

Describes the space.

Describes the space.

Sourcemodule DescribeStudioLifecycleConfigRequest = Awso_sagemaker.Values.DescribeStudioLifecycleConfigRequest

Describes the Amazon SageMaker AI Studio Lifecycle Configuration.

Sourcemodule DescribeStudioLifecycleConfigResponse = Awso_sagemaker.Values.DescribeStudioLifecycleConfigResponse

Describes the Amazon SageMaker AI Studio Lifecycle Configuration.

Sourcemodule DescribeSubscribedWorkteamRequest = Awso_sagemaker.Values.DescribeSubscribedWorkteamRequest

Gets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the Amazon Web Services Marketplace.

Describes a work team of a vendor that does the labelling job.

Sourcemodule DescribeSubscribedWorkteamResponse = Awso_sagemaker.Values.DescribeSubscribedWorkteamResponse

Gets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the Amazon Web Services Marketplace.

Sourcemodule DescribeTrainingJobRequest = Awso_sagemaker.Values.DescribeTrainingJobRequest

Returns information about a training job. Some of the attributes below only appear if the training job successfully starts. If the training job fails, TrainingJobStatus is Failed and, depending on the FailureReason, attributes like TrainingStartTime, TrainingTimeInSeconds, TrainingEndTime, and BillableTimeInSeconds may not be present in the response.

Sourcemodule ResourceRetainedBillableTimeInSeconds = Awso_sagemaker.Values.ResourceRetainedBillableTimeInSeconds

Optional. Indicates how many seconds the resource stayed in ResourceRetained state. Populated only after resource reaches ResourceReused or ResourceReleased state.

Status and billing information about the warm pool.

TrainingProgressInfo relevant fields

The serverless training job progress information.

An array element of SecondaryStatusTransitions for DescribeTrainingJob. It provides additional details about a status that the training job has transitioned through. A training job can be in one of several states, for example, starting, downloading, training, or uploading. Within each state, there are a number of intermediate states. For example, within the starting state, SageMaker could be starting the training job or launching the ML instances. These transitional states are referred to as the job's secondary status.

Sourcemodule SecondaryStatusTransitions = Awso_sagemaker.Values.SecondaryStatusTransitions
Sourcemodule ProfilerRuleEvaluationStatus = Awso_sagemaker.Values.ProfilerRuleEvaluationStatus

Information about the status of the rule evaluation.

Sourcemodule ProfilerRuleEvaluationStatuses = Awso_sagemaker.Values.ProfilerRuleEvaluationStatuses

The MLflow details of this job.

The name, value, and date and time of a metric that was emitted to Amazon CloudWatch.

include module type of struct include Awso_sagemaker.Values_2 end
Sourcemodule DescribeTrainingJobResponse = Awso_sagemaker.Values.DescribeTrainingJobResponse

Returns information about a training job. Some of the attributes below only appear if the training job successfully starts. If the training job fails, TrainingJobStatus is Failed and, depending on the FailureReason, attributes like TrainingStartTime, TrainingTimeInSeconds, TrainingEndTime, and BillableTimeInSeconds may not be present in the response.

Sourcemodule DescribeTrainingPlanExtensionHistoryRequest = Awso_sagemaker.Values.DescribeTrainingPlanExtensionHistoryRequest

Retrieves the extension history for a specified training plan. The response includes details about each extension, such as the offering ID, start and end dates, status, payment status, and cost information.

Sourcemodule TrainingPlanExtensionOfferingId = Awso_sagemaker.Values.TrainingPlanExtensionOfferingId
Sourcemodule TrainingPlanExtensionDurationHours = Awso_sagemaker.Values.TrainingPlanExtensionDurationHours

Details about an extension to a training plan, including the offering ID, dates, status, and cost information.

Sourcemodule DescribeTrainingPlanExtensionHistoryResponse = Awso_sagemaker.Values.DescribeTrainingPlanExtensionHistoryResponse

Retrieves the extension history for a specified training plan. The response includes details about each extension, such as the offering ID, start and end dates, status, payment status, and cost information.

Sourcemodule DescribeTrainingPlanRequest = Awso_sagemaker.Values.DescribeTrainingPlanRequest

Retrieves detailed information about a specific training plan.

Sourcemodule TrainingPlanDurationMinutes = Awso_sagemaker.Values.TrainingPlanDurationMinutes

Details of a reserved capacity for the training plan. For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see CreateTrainingPlan .

Sourcemodule DescribeTrainingPlanResponse = Awso_sagemaker.Values.DescribeTrainingPlanResponse

Retrieves detailed information about a specific training plan.

Sourcemodule DescribeTransformJobRequest = Awso_sagemaker.Values.DescribeTransformJobRequest

Returns information about a transform job.

Sourcemodule DescribeTransformJobResponse = Awso_sagemaker.Values.DescribeTransformJobResponse

Returns information about a transform job.

Sourcemodule DescribeTrialComponentRequest = Awso_sagemaker.Values.DescribeTrialComponentRequest

Provides a list of a trials component's properties.

The Amazon Resource Name (ARN) and job type of the source of a trial component.

Sourcemodule TrialComponentMetricSummary = Awso_sagemaker.Values.TrialComponentMetricSummary

A summary of the metrics of a trial component.

Sourcemodule TrialComponentMetricSummaries = Awso_sagemaker.Values.TrialComponentMetricSummaries
Sourcemodule DescribeTrialComponentResponse = Awso_sagemaker.Values.DescribeTrialComponentResponse

Provides a list of a trials component's properties.

Provides a list of a trial's properties.

The source of the trial.

Provides a list of a trial's properties.

Sourcemodule DescribeUserProfileRequest = Awso_sagemaker.Values.DescribeUserProfileRequest

Describes a user profile. For more information, see CreateUserProfile.

Sourcemodule DescribeUserProfileResponse = Awso_sagemaker.Values.DescribeUserProfileResponse

Describes a user profile. For more information, see CreateUserProfile.

Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs). Allowable IP address ranges are the IP addresses that workers can use to access tasks. This operation applies only to private workforces.

Sourcemodule WorkforceVpcConfigResponse = Awso_sagemaker.Values.WorkforceVpcConfigResponse

A VpcConfig object that specifies the VPC that you want your workforce to connect to.

Your OIDC IdP workforce configuration.

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.

Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs). Allowable IP address ranges are the IP addresses that workers can use to access tasks. This operation applies only to private workforces.

Gets information about a specific work team. You can see information such as the creation date, the last updated date, membership information, and the work team's Amazon Resource Name (ARN).

Provides details about a labeling work team.

Gets information about a specific work team. You can see information such as the creation date, the last updated date, membership information, and the work team's Amazon Resource Name (ARN).

Sourcemodule ProductionVariantServerlessUpdateConfig = Awso_sagemaker.Values.ProductionVariantServerlessUpdateConfig

Specifies the serverless update concurrency configuration for an endpoint variant.

Specifies weight and capacity values for a production variant.

Sourcemodule DesiredWeightAndCapacityList = Awso_sagemaker.Values.DesiredWeightAndCapacityList
Sourcemodule DetachClusterNodeVolumeRequest = Awso_sagemaker.Values.DetachClusterNodeVolumeRequest

Detaches your Amazon Elastic Block Store (Amazon EBS) volume from a node in your EKS orchestrated SageMaker HyperPod cluster. This API works with the Amazon Elastic Block Store (Amazon EBS) Container Storage Interface (CSI) driver to manage the lifecycle of persistent storage in your HyperPod EKS clusters.

Sourcemodule DetachClusterNodeVolumeResponse = Awso_sagemaker.Values.DetachClusterNodeVolumeResponse

Detaches your Amazon Elastic Block Store (Amazon EBS) volume from a node in your EKS orchestrated SageMaker HyperPod cluster. This API works with the Amazon Elastic Block Store (Amazon EBS) Container Storage Interface (CSI) driver to manage the lifecycle of persistent storage in your HyperPod EKS clusters.

Information of a particular device.

Contains information summarizing device details and deployment status.

Summary of the device fleet.

Status of devices.

Summary of model on edge device.

Summary of the device.

Sourcemodule DisableSagemakerServicecatalogPortfolioInput = Awso_sagemaker.Values.DisableSagemakerServicecatalogPortfolioInput

Disables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.

Sourcemodule DisableSagemakerServicecatalogPortfolioOutput = Awso_sagemaker.Values.DisableSagemakerServicecatalogPortfolioOutput

Disables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.

Sourcemodule DisassociateAdditionalCodeRepositories = Awso_sagemaker.Values.DisassociateAdditionalCodeRepositories
Sourcemodule DisassociateDefaultCodeRepository = Awso_sagemaker.Values.DisassociateDefaultCodeRepository
Sourcemodule DisassociateNotebookInstanceAcceleratorTypes = Awso_sagemaker.Values.DisassociateNotebookInstanceAcceleratorTypes
Sourcemodule DisassociateNotebookInstanceLifecycleConfig = Awso_sagemaker.Values.DisassociateNotebookInstanceLifecycleConfig
Sourcemodule DisassociateTrialComponentRequest = Awso_sagemaker.Values.DisassociateTrialComponentRequest

Disassociates a trial component from a trial. This doesn't effect other trials the component is associated with. Before you can delete a component, you must disassociate the component from all trials it is associated with. To associate a trial component with a trial, call the AssociateTrialComponent API. To get a list of the trials a component is associated with, use the Search API. Specify ExperimentTrialComponent for the Resource parameter. The list appears in the response under Results.TrialComponent.Parents.

Sourcemodule DisassociateTrialComponentResponse = Awso_sagemaker.Values.DisassociateTrialComponentResponse

Disassociates a trial component from a trial. This doesn't effect other trials the component is associated with. Before you can delete a component, you must disassociate the component from all trials it is associated with. To associate a trial component with a trial, call the AssociateTrialComponent API. To get a list of the trials a component is associated with, use the Search API. Specify ExperimentTrialComponent for the Resource parameter. The list appears in the response under Results.TrialComponent.Parents.

The domain's details.

Sourcemodule RStudioServerProDomainSettingsForUpdate = Awso_sagemaker.Values.RStudioServerProDomainSettingsForUpdate

A collection of settings that update the current configuration for the RStudioServerPro Domain-level app.

A collection of Domain configuration settings to update.

Sourcemodule PredefinedMetricSpecification = Awso_sagemaker.Values.PredefinedMetricSpecification

A specification for a predefined metric.

An object containing information about a metric.

Sourcemodule TargetTrackingScalingPolicyConfiguration = Awso_sagemaker.Values.TargetTrackingScalingPolicyConfiguration

A target tracking scaling policy. Includes support for predefined or customized metrics. When using the PutScalingPolicy API, this parameter is required when you are creating a policy with the policy type TargetTrackingScaling.

An object containing a recommended scaling policy.

Sourcemodule DynamicScalingConfiguration = Awso_sagemaker.Values.DynamicScalingConfiguration

An object with the recommended values for you to specify when creating an autoscaling policy.

The configurations and outcomes of an Amazon EMR step execution.

A directed edge connecting two lineage entities.

Contains information summarizing an edge deployment plan.

Sourcemodule EdgeDeploymentPlanSummaries = Awso_sagemaker.Values.EdgeDeploymentPlanSummaries

Status of edge devices with this model.

Summary of edge packaging job.

Sourcemodule EnableSagemakerServicecatalogPortfolioInput = Awso_sagemaker.Values.EnableSagemakerServicecatalogPortfolioInput

Enables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.

Sourcemodule EnableSagemakerServicecatalogPortfolioOutput = Awso_sagemaker.Values.EnableSagemakerServicecatalogPortfolioOutput

Enables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.

A schedule for a model monitoring job. For information about model monitor, see Amazon SageMaker Model Monitor.

A hosted endpoint for real-time inference.

Sourcemodule EndpointConfigNameContains = Awso_sagemaker.Values.EndpointConfigNameContains
Sourcemodule EndpointConfigStepMetadata = Awso_sagemaker.Values.EndpointConfigStepMetadata

Metadata for an endpoint configuration step.

Provides summary information for an endpoint configuration.

Metadata for an endpoint step.

Provides summary information for an endpoint.

The properties of an experiment as returned by the Search API. For information about experiments, see the CreateExperiment API.

A summary of the properties of an experiment. To get the complete set of properties, call the DescribeExperiment API and provide the ExperimentName.

Extends an existing training plan by purchasing an extension offering. This allows you to add additional compute capacity time to your training plan without creating a new plan or reconfiguring your workloads. To find available extension offerings, use the SearchTrainingPlanOfferings API with the TrainingPlanArn parameter. To view the history of extensions for a training plan, use the DescribeTrainingPlanExtensionHistory API.

Sourcemodule ExtendTrainingPlanResponse = Awso_sagemaker.Values.ExtendTrainingPlanResponse

Extends an existing training plan by purchasing an extension offering. This allows you to add additional compute capacity time to your training plan without creating a new plan or reconfiguring your workloads. To find available extension offerings, use the SearchTrainingPlanOfferings API with the TrainingPlanArn parameter. To view the history of extensions for a training plan, use the DescribeTrainingPlanExtensionHistory API.

The container for the metadata for Fail step.

Amazon SageMaker Feature Store stores features in a collection called Feature Group. A Feature Group can be visualized as a table which has rows, with a unique identifier for each row where each column in the table is a feature. In principle, a Feature Group is composed of features and values per features.

The name, ARN, CreationTime, FeatureGroup values, LastUpdatedTime and EnableOnlineStorage status of a FeatureGroup.

The metadata for a feature. It can either be metadata that you specify, or metadata that is updated automatically.

A conditional statement for a search expression that includes a resource property, a Boolean operator, and a value. Resources that match the statement are returned in the results from the Search API. If you specify a Value, but not an Operator, SageMaker uses the equals operator. In search, there are several property types: Metrics To define a metric filter, enter a value using the form "Metrics.<name>", where <name> is a metric name. For example, the following filter searches for training jobs with an "accuracy" metric greater than "0.9": { "Name": "Metrics.accuracy", "Operator": "GreaterThan", "Value": "0.9" } HyperParameters To define a hyperparameter filter, enter a value with the form "HyperParameters.<name>". Decimal hyperparameter values are treated as a decimal in a comparison if the specified Value is also a decimal value. If the specified Value is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a "learning_rate" hyperparameter that is less than "0.5": { "Name": "HyperParameters.learning_rate", "Operator": "LessThan", "Value": "0.5" } Tags To define a tag filter, enter a value with the form Tags.<key>.

Contains summary information about the flow definition.

Sourcemodule GetDeviceFleetReportRequest = Awso_sagemaker.Values.GetDeviceFleetReportRequest

Describes a fleet.

Sourcemodule GetDeviceFleetReportResponse = Awso_sagemaker.Values.GetDeviceFleetReportResponse

Describes a fleet.

Sourcemodule GetLineageGroupPolicyRequest = Awso_sagemaker.Values.GetLineageGroupPolicyRequest

The resource policy for the lineage group.

Sourcemodule GetLineageGroupPolicyResponse = Awso_sagemaker.Values.GetLineageGroupPolicyResponse

The resource policy for the lineage group.

Sourcemodule GetModelPackageGroupPolicyInput = Awso_sagemaker.Values.GetModelPackageGroupPolicyInput

Gets a resource policy that manages access for a model group. For information about resource policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..

Sourcemodule GetModelPackageGroupPolicyOutput = Awso_sagemaker.Values.GetModelPackageGroupPolicyOutput

Gets a resource policy that manages access for a model group. For information about resource policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..

Sourcemodule GetSagemakerServicecatalogPortfolioStatusInput = Awso_sagemaker.Values.GetSagemakerServicecatalogPortfolioStatusInput

Gets the status of Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.

Sourcemodule SagemakerServicecatalogStatus = Awso_sagemaker.Values.SagemakerServicecatalogStatus
Sourcemodule GetSagemakerServicecatalogPortfolioStatusOutput = Awso_sagemaker.Values.GetSagemakerServicecatalogPortfolioStatusOutput

Gets the status of Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.

Sourcemodule UtilizationPercentagePerCore = Awso_sagemaker.Values.UtilizationPercentagePerCore

An object where you specify the anticipated traffic pattern for an endpoint.

Sourcemodule GetScalingConfigurationRecommendationRequest = Awso_sagemaker.Values.GetScalingConfigurationRecommendationRequest

Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job. Returns recommendations for autoscaling policies that you can apply to your SageMaker endpoint.

The metric for a scaling policy.

Sourcemodule GetScalingConfigurationRecommendationResponse = Awso_sagemaker.Values.GetScalingConfigurationRecommendationResponse

Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job. Returns recommendations for autoscaling policies that you can apply to your SageMaker endpoint.

Part of the SuggestionQuery type. Specifies a hint for retrieving property names that begin with the specified text.

Specified in the GetSearchSuggestions request. Limits the property names that are included in the response.

Sourcemodule GetSearchSuggestionsRequest = Awso_sagemaker.Values.GetSearchSuggestionsRequest

An auto-complete API for the search functionality in the SageMaker console. It returns suggestions of possible matches for the property name to use in Search queries. Provides suggestions for HyperParameters, Tags, and Metrics.

A property name returned from a GetSearchSuggestions call that specifies a value in the PropertyNameQuery field.

Sourcemodule PropertyNameSuggestionList = Awso_sagemaker.Values.PropertyNameSuggestionList
Sourcemodule GetSearchSuggestionsResponse = Awso_sagemaker.Values.GetSearchSuggestionsResponse

An auto-complete API for the search functionality in the SageMaker console. It returns suggestions of possible matches for the property name to use in Search queries. Provides suggestions for HyperParameters, Tags, and Metrics.

Specifies configuration details for a Git repository when the repository is updated.

Information about hub content.

Information about a hub.

Container for human task user interface information.

Sourcemodule HyperParameterTrainingJobSummaries = Awso_sagemaker.Values.HyperParameterTrainingJobSummaries
Sourcemodule HyperParameterTuningJobSearchEntity = Awso_sagemaker.Values.HyperParameterTuningJobSearchEntity

An entity returned by the SearchRecord API containing the properties of a hyperparameter tuning job.

Sourcemodule HyperParameterTuningJobSortByOptions = Awso_sagemaker.Values.HyperParameterTuningJobSortByOptions
Sourcemodule HyperParameterTuningJobSummary = Awso_sagemaker.Values.HyperParameterTuningJobSummary

Provides summary information about a hyperparameter tuning job.

Sourcemodule HyperParameterTuningJobSummaries = Awso_sagemaker.Values.HyperParameterTuningJobSummaries

A SageMaker AI image. A SageMaker AI image represents a set of container images that are derived from a common base container image. Each of these container images is represented by a SageMaker AI ImageVersion.

A version of a SageMaker AI Image. A version represents an existing container image.

Import hub content.

Import hub content.

Sourcemodule IncludeNodeLogicalIdsBoolean = Awso_sagemaker.Values.IncludeNodeLogicalIdsBoolean
Sourcemodule InferenceComponentMetadata = Awso_sagemaker.Values.InferenceComponentMetadata

The metadata of the inference component.

Sourcemodule InferenceComponentNameContains = Awso_sagemaker.Values.InferenceComponentNameContains

A summary of the properties of an inference component.

Sourcemodule InferenceComponentSummaryList = Awso_sagemaker.Values.InferenceComponentSummaryList
Sourcemodule InferenceExperimentSummary = Awso_sagemaker.Values.InferenceExperimentSummary

Lists a summary of properties of an inference experiment.

Sourcemodule InferenceExperimentStopDesiredState = Awso_sagemaker.Values.InferenceExperimentStopDesiredState
Sourcemodule InferenceRecommendationsJob = Awso_sagemaker.Values.InferenceRecommendationsJob

A structure that contains a list of recommendation jobs.

Sourcemodule RecommendationFailureReason = Awso_sagemaker.Values.RecommendationFailureReason
Sourcemodule RecommendationJobInferenceBenchmark = Awso_sagemaker.Values.RecommendationJobInferenceBenchmark

The details for a specific benchmark from an Inference Recommender job.

Sourcemodule InferenceRecommendationsJobStep = Awso_sagemaker.Values.InferenceRecommendationsJobStep

A returned array object for the Steps response field in the ListInferenceRecommendationsJobSteps API command.

Sourcemodule InferenceRecommendationsJobSteps = Awso_sagemaker.Values.InferenceRecommendationsJobSteps
Sourcemodule InferenceRecommendationsJobs = Awso_sagemaker.Values.InferenceRecommendationsJobs

Provides counts for human-labeled tasks in the labeling job.

Sourcemodule LabelingJobForWorkteamSummary = Awso_sagemaker.Values.LabelingJobForWorkteamSummary

Provides summary information for a work team.

Sourcemodule LabelingJobForWorkteamSummaryList = Awso_sagemaker.Values.LabelingJobForWorkteamSummaryList

Provides summary information about a labeling job.

Metadata for a Lambda step.

Lists a summary of the properties of a lineage group. A lineage group provides a group of shareable lineage entity resources.

The metadata that tracks relationships between ML artifacts, actions, and contexts.

Sourcemodule ListAIBenchmarkJobsRequest = Awso_sagemaker.Values.ListAIBenchmarkJobsRequest

Returns a list of AI benchmark jobs in your account. You can filter the results by name, status, and creation time, and sort the results. The response is paginated.

Sourcemodule ListAIBenchmarkJobsResponse = Awso_sagemaker.Values.ListAIBenchmarkJobsResponse

Returns a list of AI benchmark jobs in your account. You can filter the results by name, status, and creation time, and sort the results. The response is paginated.

Sourcemodule ListAIRecommendationJobsSortBy = Awso_sagemaker.Values.ListAIRecommendationJobsSortBy
Sourcemodule ListAIRecommendationJobsRequest = Awso_sagemaker.Values.ListAIRecommendationJobsRequest

Returns a list of AI recommendation jobs in your account. You can filter the results by name, status, and creation time, and sort the results. The response is paginated.

Sourcemodule ListAIRecommendationJobsResponse = Awso_sagemaker.Values.ListAIRecommendationJobsResponse

Returns a list of AI recommendation jobs in your account. You can filter the results by name, status, and creation time, and sort the results. The response is paginated.

Sourcemodule ListAIWorkloadConfigsSortBy = Awso_sagemaker.Values.ListAIWorkloadConfigsSortBy
Sourcemodule ListAIWorkloadConfigsRequest = Awso_sagemaker.Values.ListAIWorkloadConfigsRequest

Returns a list of AI workload configurations in your account. You can filter the results by name and creation time, and sort the results. The response is paginated.

Sourcemodule ListAIWorkloadConfigsResponse = Awso_sagemaker.Values.ListAIWorkloadConfigsResponse

Returns a list of AI workload configurations in your account. You can filter the results by name and creation time, and sort the results. The response is paginated.

Lists the actions in your account and their properties.

Lists the actions in your account and their properties.

Lists the machine learning algorithms that have been created.

Lists the machine learning algorithms that have been created.

Lists the aliases of a specified image or image version.

Lists the aliases of a specified image or image version.

Sourcemodule ListAppImageConfigsRequest = Awso_sagemaker.Values.ListAppImageConfigsRequest

Lists the AppImageConfigs in your account and their properties. The list can be filtered by creation time or modified time, and whether the AppImageConfig name contains a specified string.

Sourcemodule ListAppImageConfigsResponse = Awso_sagemaker.Values.ListAppImageConfigsResponse

Lists the AppImageConfigs in your account and their properties. The list can be filtered by creation time or modified time, and whether the AppImageConfig name contains a specified string.

Lists apps.

Lists apps.

Lists the artifacts in your account and their properties.

Lists the artifacts in your account and their properties.

Lists the associations in your account and their properties.

Lists the associations in your account and their properties.

Request a list of jobs.

Request a list of jobs.

Sourcemodule ListCandidatesForAutoMLJobRequest = Awso_sagemaker.Values.ListCandidatesForAutoMLJobRequest

List the candidates created for the job.

Sourcemodule ListCandidatesForAutoMLJobResponse = Awso_sagemaker.Values.ListCandidatesForAutoMLJobResponse

List the candidates created for the job.

Retrieves a list of event summaries for a specified HyperPod cluster. The operation supports filtering, sorting, and pagination of results. This functionality is only supported when the NodeProvisioningMode is set to Continuous.

Retrieves a list of event summaries for a specified HyperPod cluster. The operation supports filtering, sorting, and pagination of results. This functionality is only supported when the NodeProvisioningMode is set to Continuous.

Retrieves the list of instances (also called nodes interchangeably) in a SageMaker HyperPod cluster.

Retrieves the list of instances (also called nodes interchangeably) in a SageMaker HyperPod cluster.

Sourcemodule SortClusterSchedulerConfigBy = Awso_sagemaker.Values.SortClusterSchedulerConfigBy
Sourcemodule ListClusterSchedulerConfigsRequest = Awso_sagemaker.Values.ListClusterSchedulerConfigsRequest

List the cluster policy configurations.

Sourcemodule ListClusterSchedulerConfigsResponse = Awso_sagemaker.Values.ListClusterSchedulerConfigsResponse

List the cluster policy configurations.

Retrieves the list of SageMaker HyperPod clusters.

Retrieves the list of SageMaker HyperPod clusters.

Gets a list of the Git repositories in your account.

Sourcemodule ListCodeRepositoriesOutput = Awso_sagemaker.Values.ListCodeRepositoriesOutput

Gets a list of the Git repositories in your account.

Sourcemodule ListCompilationJobsRequest = Awso_sagemaker.Values.ListCompilationJobsRequest

Lists model compilation jobs that satisfy various filters. To create a model compilation job, use CreateCompilationJob. To get information about a particular model compilation job you have created, use DescribeCompilationJob.

Sourcemodule ListCompilationJobsResponse = Awso_sagemaker.Values.ListCompilationJobsResponse

Lists model compilation jobs that satisfy various filters. To create a model compilation job, use CreateCompilationJob. To get information about a particular model compilation job you have created, use DescribeCompilationJob.

List the resource allocation definitions.

List the resource allocation definitions.

Lists the contexts in your account and their properties.

Lists the contexts in your account and their properties.

Sourcemodule MonitoringJobDefinitionSortKey = Awso_sagemaker.Values.MonitoringJobDefinitionSortKey
Sourcemodule ListDataQualityJobDefinitionsRequest = Awso_sagemaker.Values.ListDataQualityJobDefinitionsRequest

Lists the data quality job definitions in your account.

Sourcemodule MonitoringJobDefinitionSummary = Awso_sagemaker.Values.MonitoringJobDefinitionSummary

Summary information about a monitoring job.

Sourcemodule MonitoringJobDefinitionSummaryList = Awso_sagemaker.Values.MonitoringJobDefinitionSummaryList
Sourcemodule ListDataQualityJobDefinitionsResponse = Awso_sagemaker.Values.ListDataQualityJobDefinitionsResponse

Lists the data quality job definitions in your account.

Returns a list of devices in the fleet.

Returns a list of devices in the fleet.

A list of devices.

A list of devices.

Lists the domains.

Lists the domains.

Sourcemodule ListEdgeDeploymentPlansSortBy = Awso_sagemaker.Values.ListEdgeDeploymentPlansSortBy
Sourcemodule ListEdgeDeploymentPlansRequest = Awso_sagemaker.Values.ListEdgeDeploymentPlansRequest

Lists all edge deployment plans.

Sourcemodule ListEdgeDeploymentPlansResponse = Awso_sagemaker.Values.ListEdgeDeploymentPlansResponse

Lists all edge deployment plans.

Sourcemodule ListEdgePackagingJobsSortBy = Awso_sagemaker.Values.ListEdgePackagingJobsSortBy
Sourcemodule ListEdgePackagingJobsRequest = Awso_sagemaker.Values.ListEdgePackagingJobsRequest

Returns a list of edge packaging jobs.

Sourcemodule ListEdgePackagingJobsResponse = Awso_sagemaker.Values.ListEdgePackagingJobsResponse

Returns a list of edge packaging jobs.

Lists endpoint configurations.

Lists endpoint configurations.

Lists endpoints.

Lists endpoints.

Lists all the experiments in your account. The list can be filtered to show only experiments that were created in a specific time range. The list can be sorted by experiment name or creation time.

Lists all the experiments in your account. The list can be filtered to show only experiments that were created in a specific time range. The list can be sorted by experiment name or creation time.

List FeatureGroups based on given filter and order.

List FeatureGroups based on given filter and order.

Sourcemodule ListFlowDefinitionsRequest = Awso_sagemaker.Values.ListFlowDefinitionsRequest

Returns information about the flow definitions in your account.

Sourcemodule ListFlowDefinitionsResponse = Awso_sagemaker.Values.ListFlowDefinitionsResponse

Returns information about the flow definitions in your account.

Sourcemodule ListHubContentVersionsRequest = Awso_sagemaker.Values.ListHubContentVersionsRequest

List hub content versions.

Sourcemodule ListHubContentVersionsResponse = Awso_sagemaker.Values.ListHubContentVersionsResponse

List hub content versions.

List the contents of a hub.

List the contents of a hub.

List all existing hubs.

List all existing hubs.

Returns information about the human task user interfaces in your account.

Returns information about the human task user interfaces in your account.

Sourcemodule ListHyperParameterTuningJobsRequest = Awso_sagemaker.Values.ListHyperParameterTuningJobsRequest

Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.

Sourcemodule ListHyperParameterTuningJobsResponse = Awso_sagemaker.Values.ListHyperParameterTuningJobsResponse

Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.

Lists the versions of a specified image and their properties. The list can be filtered by creation time or modified time.

Lists the versions of a specified image and their properties. The list can be filtered by creation time or modified time.

Lists the images in your account and their properties. The list can be filtered by creation time or modified time, and whether the image name contains a specified string.

Lists the images in your account and their properties. The list can be filtered by creation time or modified time, and whether the image name contains a specified string.

Sourcemodule ListInferenceComponentsInput = Awso_sagemaker.Values.ListInferenceComponentsInput

Lists the inference components in your account and their properties.

Sourcemodule ListInferenceComponentsOutput = Awso_sagemaker.Values.ListInferenceComponentsOutput

Lists the inference components in your account and their properties.

Sourcemodule SortInferenceExperimentsBy = Awso_sagemaker.Values.SortInferenceExperimentsBy
Sourcemodule ListInferenceExperimentsRequest = Awso_sagemaker.Values.ListInferenceExperimentsRequest

Returns the list of all inference experiments.

Sourcemodule ListInferenceExperimentsResponse = Awso_sagemaker.Values.ListInferenceExperimentsResponse

Returns the list of all inference experiments.

Sourcemodule ListInferenceRecommendationsJobStepsRequest = Awso_sagemaker.Values.ListInferenceRecommendationsJobStepsRequest

Returns a list of the subtasks for an Inference Recommender job. The supported subtasks are benchmarks, which evaluate the performance of your model on different instance types.

Sourcemodule ListInferenceRecommendationsJobStepsResponse = Awso_sagemaker.Values.ListInferenceRecommendationsJobStepsResponse

Returns a list of the subtasks for an Inference Recommender job. The supported subtasks are benchmarks, which evaluate the performance of your model on different instance types.

Sourcemodule ListInferenceRecommendationsJobsSortBy = Awso_sagemaker.Values.ListInferenceRecommendationsJobsSortBy
Sourcemodule ListInferenceRecommendationsJobsRequest = Awso_sagemaker.Values.ListInferenceRecommendationsJobsRequest

Lists recommendation jobs that satisfy various filters.

Sourcemodule ListInferenceRecommendationsJobsResponse = Awso_sagemaker.Values.ListInferenceRecommendationsJobsResponse

Lists recommendation jobs that satisfy various filters.

Sourcemodule ListLabelingJobsForWorkteamSortByOptions = Awso_sagemaker.Values.ListLabelingJobsForWorkteamSortByOptions
Sourcemodule ListLabelingJobsForWorkteamRequest = Awso_sagemaker.Values.ListLabelingJobsForWorkteamRequest

Gets a list of labeling jobs assigned to a specified work team.

Sourcemodule ListLabelingJobsForWorkteamResponse = Awso_sagemaker.Values.ListLabelingJobsForWorkteamResponse

Gets a list of labeling jobs assigned to a specified work team.

Gets a list of labeling jobs.

Gets a list of labeling jobs.

Sourcemodule ListLineageEntityParameterKey = Awso_sagemaker.Values.ListLineageEntityParameterKey

A list of lineage groups shared with your Amazon Web Services account. For more information, see Cross-Account Lineage Tracking in the Amazon SageMaker Developer Guide.

A list of lineage groups shared with your Amazon Web Services account. For more information, see Cross-Account Lineage Tracking in the Amazon SageMaker Developer Guide.

Lists all MLflow Apps

The summary of the Mlflow App to list.

Lists all MLflow Apps

Sourcemodule ListMlflowTrackingServersRequest = Awso_sagemaker.Values.ListMlflowTrackingServersRequest

Lists all MLflow Tracking Servers.

The summary of the tracking server to list.

Sourcemodule ListMlflowTrackingServersResponse = Awso_sagemaker.Values.ListMlflowTrackingServersResponse

Lists all MLflow Tracking Servers.

Sourcemodule ListModelBiasJobDefinitionsRequest = Awso_sagemaker.Values.ListModelBiasJobDefinitionsRequest

Lists model bias jobs definitions that satisfy various filters.

Sourcemodule ListModelBiasJobDefinitionsResponse = Awso_sagemaker.Values.ListModelBiasJobDefinitionsResponse

Lists model bias jobs definitions that satisfy various filters.

Sourcemodule ModelCardExportJobSortOrder = Awso_sagemaker.Values.ModelCardExportJobSortOrder
Sourcemodule ListModelCardExportJobsRequest = Awso_sagemaker.Values.ListModelCardExportJobsRequest

List the export jobs for the Amazon SageMaker Model Card.

The summary of the Amazon SageMaker Model Card export job.

Sourcemodule ModelCardExportJobSummaryList = Awso_sagemaker.Values.ModelCardExportJobSummaryList
Sourcemodule ListModelCardExportJobsResponse = Awso_sagemaker.Values.ListModelCardExportJobsResponse

List the export jobs for the Amazon SageMaker Model Card.

Sourcemodule ListModelCardVersionsRequest = Awso_sagemaker.Values.ListModelCardVersionsRequest

List existing versions of an Amazon SageMaker Model Card.

A summary of a specific version of the model card.

Sourcemodule ModelCardVersionSummaryList = Awso_sagemaker.Values.ModelCardVersionSummaryList
Sourcemodule ListModelCardVersionsResponse = Awso_sagemaker.Values.ListModelCardVersionsResponse

List existing versions of an Amazon SageMaker Model Card.

List existing model cards.

A summary of the model card.

List existing model cards.

Sourcemodule ListModelExplainabilityJobDefinitionsRequest = Awso_sagemaker.Values.ListModelExplainabilityJobDefinitionsRequest

Lists model explainability job definitions that satisfy various filters.

Sourcemodule ListModelExplainabilityJobDefinitionsResponse = Awso_sagemaker.Values.ListModelExplainabilityJobDefinitionsResponse

Lists model explainability job definitions that satisfy various filters.

Part of the search expression. You can specify the name and value (domain, task, framework, framework version, task, and model).

Sourcemodule ModelMetadataSearchExpression = Awso_sagemaker.Values.ModelMetadataSearchExpression

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

Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos.

A summary of the model metadata.

Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos.

Sourcemodule ListModelPackageGroupsInput = Awso_sagemaker.Values.ListModelPackageGroupsInput

Gets a list of the model groups in your Amazon Web Services account.

Summary information about a model group.

Sourcemodule ModelPackageGroupSummaryList = Awso_sagemaker.Values.ModelPackageGroupSummaryList
Sourcemodule ListModelPackageGroupsOutput = Awso_sagemaker.Values.ListModelPackageGroupsOutput

Gets a list of the model groups in your Amazon Web Services account.

Lists the model packages that have been created.

Provides summary information about a model package.

Lists the model packages that have been created.

Sourcemodule ListModelQualityJobDefinitionsRequest = Awso_sagemaker.Values.ListModelQualityJobDefinitionsRequest

Gets a list of model quality monitoring job definitions in your account.

Sourcemodule ListModelQualityJobDefinitionsResponse = Awso_sagemaker.Values.ListModelQualityJobDefinitionsResponse

Gets a list of model quality monitoring job definitions in your account.

Lists models created with the CreateModel API.

Provides summary information about a model.

Lists models created with the CreateModel API.

Sourcemodule MonitoringAlertHistorySortKey = Awso_sagemaker.Values.MonitoringAlertHistorySortKey
Sourcemodule ListMonitoringAlertHistoryRequest = Awso_sagemaker.Values.ListMonitoringAlertHistoryRequest

Gets a list of past alerts in a model monitoring schedule.

Sourcemodule MonitoringAlertHistorySummary = Awso_sagemaker.Values.MonitoringAlertHistorySummary

Provides summary information of an alert's history.

Sourcemodule MonitoringAlertHistoryList = Awso_sagemaker.Values.MonitoringAlertHistoryList
Sourcemodule ListMonitoringAlertHistoryResponse = Awso_sagemaker.Values.ListMonitoringAlertHistoryResponse

Gets a list of past alerts in a model monitoring schedule.

Sourcemodule ListMonitoringAlertsRequest = Awso_sagemaker.Values.ListMonitoringAlertsRequest

Gets the alerts for a single monitoring schedule.

Sourcemodule MonitoringEvaluationPeriod = Awso_sagemaker.Values.MonitoringEvaluationPeriod
Sourcemodule MonitoringDatapointsToAlert = Awso_sagemaker.Values.MonitoringDatapointsToAlert
Sourcemodule ModelDashboardIndicatorAction = Awso_sagemaker.Values.ModelDashboardIndicatorAction

An alert action taken to light up an icon on the Amazon SageMaker Model Dashboard when an alert goes into InAlert status.

A list of alert actions taken in response to an alert going into InAlert status.

Provides summary information about a monitor alert.

Sourcemodule MonitoringAlertSummaryList = Awso_sagemaker.Values.MonitoringAlertSummaryList
Sourcemodule ListMonitoringAlertsResponse = Awso_sagemaker.Values.ListMonitoringAlertsResponse

Gets the alerts for a single monitoring schedule.

Sourcemodule MonitoringExecutionSortKey = Awso_sagemaker.Values.MonitoringExecutionSortKey
Sourcemodule ListMonitoringExecutionsRequest = Awso_sagemaker.Values.ListMonitoringExecutionsRequest

Returns list of all monitoring job executions.

Sourcemodule MonitoringExecutionSummaryList = Awso_sagemaker.Values.MonitoringExecutionSummaryList
Sourcemodule ListMonitoringExecutionsResponse = Awso_sagemaker.Values.ListMonitoringExecutionsResponse

Returns list of all monitoring job executions.

Sourcemodule ListMonitoringSchedulesRequest = Awso_sagemaker.Values.ListMonitoringSchedulesRequest

Returns list of all monitoring schedules.

Summarizes the monitoring schedule.

Sourcemodule MonitoringScheduleSummaryList = Awso_sagemaker.Values.MonitoringScheduleSummaryList
Sourcemodule ListMonitoringSchedulesResponse = Awso_sagemaker.Values.ListMonitoringSchedulesResponse

Returns list of all monitoring schedules.

Sourcemodule NotebookInstanceLifecycleConfigSortOrder = Awso_sagemaker.Values.NotebookInstanceLifecycleConfigSortOrder
Sourcemodule NotebookInstanceLifecycleConfigSortKey = Awso_sagemaker.Values.NotebookInstanceLifecycleConfigSortKey
Sourcemodule NotebookInstanceLifecycleConfigNameContains = Awso_sagemaker.Values.NotebookInstanceLifecycleConfigNameContains
Sourcemodule ListNotebookInstanceLifecycleConfigsInput = Awso_sagemaker.Values.ListNotebookInstanceLifecycleConfigsInput

Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.

Sourcemodule NotebookInstanceLifecycleConfigSummary = Awso_sagemaker.Values.NotebookInstanceLifecycleConfigSummary

Provides a summary of a notebook instance lifecycle configuration.

Sourcemodule NotebookInstanceLifecycleConfigSummaryList = Awso_sagemaker.Values.NotebookInstanceLifecycleConfigSummaryList
Sourcemodule ListNotebookInstanceLifecycleConfigsOutput = Awso_sagemaker.Values.ListNotebookInstanceLifecycleConfigsOutput

Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.

Sourcemodule NotebookInstanceNameContains = Awso_sagemaker.Values.NotebookInstanceNameContains
Sourcemodule ListNotebookInstancesInput = Awso_sagemaker.Values.ListNotebookInstancesInput

Returns a list of the SageMaker AI notebook instances in the requester's account in an Amazon Web Services Region.

Provides summary information for an SageMaker AI notebook instance.

Sourcemodule NotebookInstanceSummaryList = Awso_sagemaker.Values.NotebookInstanceSummaryList
Sourcemodule ListNotebookInstancesOutput = Awso_sagemaker.Values.ListNotebookInstancesOutput

Returns a list of the SageMaker AI notebook instances in the requester's account in an Amazon Web Services Region.

Sourcemodule ListOptimizationJobsSortBy = Awso_sagemaker.Values.ListOptimizationJobsSortBy
Sourcemodule ListOptimizationJobsRequest = Awso_sagemaker.Values.ListOptimizationJobsRequest

Lists the optimization jobs in your account and their properties.

Summarizes an optimization job by providing some of its key properties.

Sourcemodule ListOptimizationJobsResponse = Awso_sagemaker.Values.ListOptimizationJobsResponse

Lists the optimization jobs in your account and their properties.

Lists all of the SageMaker Partner AI Apps in an account.

A subset of information related to a SageMaker Partner AI App. This information is used as part of the ListPartnerApps API response.

Lists all of the SageMaker Partner AI Apps in an account.

Sourcemodule ListPipelineExecutionStepsRequest = Awso_sagemaker.Values.ListPipelineExecutionStepsRequest

Gets a list of PipeLineExecutionStep objects.

The ARN from an execution of the current pipeline.

Metadata for a tuning step.

Metadata for a transform job step.

Metadata for a training job step.

Metadata for a register model job step.

Container for the metadata for a Quality check step. For more information, see the topic on QualityCheck step in the Amazon SageMaker Developer Guide.

Metadata for a processing job step.

Metadata for Model steps.

Sourcemodule PipelineExecutionStepMetadata = Awso_sagemaker.Values.PipelineExecutionStepMetadata

Metadata for a step execution.

An execution of a step in a pipeline.

Sourcemodule ListPipelineExecutionStepsResponse = Awso_sagemaker.Values.ListPipelineExecutionStepsResponse

Gets a list of PipeLineExecutionStep objects.

Sourcemodule ListPipelineExecutionsRequest = Awso_sagemaker.Values.ListPipelineExecutionsRequest

Gets a list of the pipeline executions.

A pipeline execution summary.

Sourcemodule PipelineExecutionSummaryList = Awso_sagemaker.Values.PipelineExecutionSummaryList
Sourcemodule ListPipelineExecutionsResponse = Awso_sagemaker.Values.ListPipelineExecutionsResponse

Gets a list of the pipeline executions.

Sourcemodule ListPipelineParametersForExecutionRequest = Awso_sagemaker.Values.ListPipelineParametersForExecutionRequest

Gets a list of parameters for a pipeline execution.

Assigns a value to a named Pipeline parameter.

Sourcemodule ListPipelineParametersForExecutionResponse = Awso_sagemaker.Values.ListPipelineParametersForExecutionResponse

Gets a list of parameters for a pipeline execution.

Sourcemodule ListPipelineVersionsRequest = Awso_sagemaker.Values.ListPipelineVersionsRequest

Gets a list of all versions of the pipeline.

The summary of the pipeline version.

Sourcemodule PipelineVersionSummaryList = Awso_sagemaker.Values.PipelineVersionSummaryList
Sourcemodule ListPipelineVersionsResponse = Awso_sagemaker.Values.ListPipelineVersionsResponse

Gets a list of all versions of the pipeline.

Gets a list of pipelines.

A summary of a pipeline.

Gets a list of pipelines.

Lists processing jobs that satisfy various filters.

Summary of information about a processing job.

Sourcemodule ListProcessingJobsResponse = Awso_sagemaker.Values.ListProcessingJobsResponse

Lists processing jobs that satisfy various filters.

Gets a list of the projects in an Amazon Web Services account.

Information about a project.

Gets a list of the projects in an Amazon Web Services account.

Sourcemodule ListResourceCatalogsRequest = Awso_sagemaker.Values.ListResourceCatalogsRequest

Lists Amazon SageMaker Catalogs based on given filters and orders. The maximum number of ResourceCatalogs viewable is 1000.

Sourcemodule ResourceCatalogDescription = Awso_sagemaker.Values.ResourceCatalogDescription

A resource catalog containing all of the resources of a specific resource type within a resource owner account. For an example on sharing the Amazon SageMaker Feature Store DefaultFeatureGroupCatalog, see Share Amazon SageMaker Catalog resource type in the Amazon SageMaker Developer Guide.

Sourcemodule ListResourceCatalogsResponse = Awso_sagemaker.Values.ListResourceCatalogsResponse

Lists Amazon SageMaker Catalogs based on given filters and orders. The maximum number of ResourceCatalogs viewable is 1000.

Lists spaces.

Sourcemodule SpaceSharingSettingsSummary = Awso_sagemaker.Values.SpaceSharingSettingsSummary

Specifies summary information about the space sharing settings.

Specifies summary information about the space settings.

Specifies summary information about the ownership settings.

The space's details.

Lists spaces.

Lists devices allocated to the stage, containing detailed device information and deployment status.

Lists devices allocated to the stage, containing detailed device information and deployment status.

Sourcemodule StudioLifecycleConfigSortKey = Awso_sagemaker.Values.StudioLifecycleConfigSortKey
Sourcemodule ListStudioLifecycleConfigsRequest = Awso_sagemaker.Values.ListStudioLifecycleConfigsRequest

Lists the Amazon SageMaker AI Studio Lifecycle Configurations in your Amazon Web Services Account.

Sourcemodule StudioLifecycleConfigDetails = Awso_sagemaker.Values.StudioLifecycleConfigDetails

Details of the Amazon SageMaker AI Studio Lifecycle Configuration.

Sourcemodule StudioLifecycleConfigsList = Awso_sagemaker.Values.StudioLifecycleConfigsList
Sourcemodule ListStudioLifecycleConfigsResponse = Awso_sagemaker.Values.ListStudioLifecycleConfigsResponse

Lists the Amazon SageMaker AI Studio Lifecycle Configurations in your Amazon Web Services Account.

Sourcemodule ListSubscribedWorkteamsRequest = Awso_sagemaker.Values.ListSubscribedWorkteamsRequest

Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.

Sourcemodule ListSubscribedWorkteamsResponse = Awso_sagemaker.Values.ListSubscribedWorkteamsResponse

Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.

Returns the tags for the specified SageMaker resource.

Returns the tags for the specified SageMaker resource.

Sourcemodule ListTrainingJobsForHyperParameterTuningJobRequest = Awso_sagemaker.Values.ListTrainingJobsForHyperParameterTuningJobRequest

Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.

Sourcemodule ListTrainingJobsForHyperParameterTuningJobResponse = Awso_sagemaker.Values.ListTrainingJobsForHyperParameterTuningJobResponse

Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.

Lists training jobs. When StatusEquals and MaxResults are set at the same time, the MaxResults number of training jobs are first retrieved ignoring the StatusEquals parameter and then they are filtered by the StatusEquals parameter, which is returned as a response. For example, if ListTrainingJobs is invoked with the following parameters: { ... MaxResults: 100, StatusEquals: InProgress ... } First, 100 trainings jobs with any status, including those other than InProgress, are selected (sorted according to the creation time, from the most current to the oldest). Next, those with a status of InProgress are returned. You can quickly test the API using the following Amazon Web Services CLI code. aws sagemaker list-training-jobs --max-results 100 --status-equals InProgress

Provides summary information about a training job.

Lists training jobs. When StatusEquals and MaxResults are set at the same time, the MaxResults number of training jobs are first retrieved ignoring the StatusEquals parameter and then they are filtered by the StatusEquals parameter, which is returned as a response. For example, if ListTrainingJobs is invoked with the following parameters: { ... MaxResults: 100, StatusEquals: InProgress ... } First, 100 trainings jobs with any status, including those other than InProgress, are selected (sorted according to the creation time, from the most current to the oldest). Next, those with a status of InProgress are returned. You can quickly test the API using the following Amazon Web Services CLI code. aws sagemaker list-training-jobs --max-results 100 --status-equals InProgress

A filter to apply when listing or searching for training plans. For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see CreateTrainingPlan .

Retrieves a list of training plans for the current account.

Details of the training plan. For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see CreateTrainingPlan .

Retrieves a list of training plans for the current account.

Lists transform jobs.

Provides a summary of a transform job. Multiple TransformJobSummary objects are returned as a list after in response to a ListTransformJobs call.

Lists transform jobs.

Sourcemodule ListTrialComponentsRequest = Awso_sagemaker.Values.ListTrialComponentsRequest

Lists the trial components in your account. You can sort the list by trial component name or creation time. You can filter the list to show only components that were created in a specific time range. You can also filter on one of the following: ExperimentName SourceArn TrialName

A summary of the properties of a trial component. To get all the properties, call the DescribeTrialComponent API and provide the TrialComponentName.

Sourcemodule ListTrialComponentsResponse = Awso_sagemaker.Values.ListTrialComponentsResponse

Lists the trial components in your account. You can sort the list by trial component name or creation time. You can filter the list to show only components that were created in a specific time range. You can also filter on one of the following: ExperimentName SourceArn TrialName

Lists the trials in your account. Specify an experiment name to limit the list to the trials that are part of that experiment. Specify a trial component name to limit the list to the trials that associated with that trial component. The list can be filtered to show only trials that were created in a specific time range. The list can be sorted by trial name or creation time.

A summary of the properties of a trial. To get the complete set of properties, call the DescribeTrial API and provide the TrialName.

Lists the trials in your account. Specify an experiment name to limit the list to the trials that are part of that experiment. Specify a trial component name to limit the list to the trials that associated with that trial component. The list can be filtered to show only trials that were created in a specific time range. The list can be sorted by trial name or creation time.

Sourcemodule ListUltraServersByReservedCapacityRequest = Awso_sagemaker.Values.ListUltraServersByReservedCapacityRequest

Lists all UltraServers that are part of a specified reserved capacity.

Represents a high-performance compute server used for distributed training in SageMaker AI. An UltraServer consists of multiple instances within a shared NVLink interconnect domain.

Sourcemodule ListUltraServersByReservedCapacityResponse = Awso_sagemaker.Values.ListUltraServersByReservedCapacityResponse

Lists all UltraServers that are part of a specified reserved capacity.

Lists user profiles.

The user profile details.

Lists user profiles.

Sourcemodule ListWorkforcesSortByOptions = Awso_sagemaker.Values.ListWorkforcesSortByOptions

Use this operation to list all private and vendor workforces in an Amazon Web Services Region. Note that you can only have one private workforce per Amazon Web Services Region.

Use this operation to list all private and vendor workforces in an Amazon Web Services Region. Note that you can only have one private workforce per Amazon Web Services Region.

Sourcemodule ListWorkteamsSortByOptions = Awso_sagemaker.Values.ListWorkteamsSortByOptions

Gets a list of private work teams that you have defined in a region. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.

Gets a list of private work teams that you have defined in a region. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.

The properties of a model as returned by the Search API.

An Amazon SageMaker Model Card.

An endpoint that hosts a model displayed in the Amazon SageMaker Model Dashboard.

A batch transform job. For information about SageMaker batch transform, see Use Batch Transform.

Sourcemodule ModelDashboardMonitoringSchedule = Awso_sagemaker.Values.ModelDashboardMonitoringSchedule

A monitoring schedule for a model displayed in the Amazon SageMaker Model Dashboard.

Sourcemodule ModelDashboardMonitoringSchedules = Awso_sagemaker.Values.ModelDashboardMonitoringSchedules

The model card for a model displayed in the Amazon SageMaker Model Dashboard.

A model displayed in the Amazon SageMaker Model Dashboard.

A container for your trained model that can be deployed for SageMaker inference. This can include inference code, artifacts, and metadata. The model package type can be one of the following. Versioned model: A part of a model package group in Model Registry. Unversioned model: Not part of a model package group and used in Amazon Web Services Marketplace. For more information, see CreateModelPackage .

A group of versioned models in the Model Registry.

A list of nested Filter objects. A resource must satisfy the conditions of all filters to be included in the results returned from the Search API. For example, to filter on a training job's InputDataConfig property with a specific channel name and S3Uri prefix, define the following filters: '{Name:"InputDataConfig.ChannelName", "Operator":"Equals", "Value":"train"}', '{Name:"InputDataConfig.DataSource.S3DataSource.S3Uri", "Operator":"Contains", "Value":"mybucket/catdata"}'

Updates the feature group online store configuration.

The trial that a trial component is associated with and the experiment the trial is part of. A component might not be associated with a trial. A component can be associated with multiple trials.

A SageMaker Model Building Pipeline instance.

An execution of a pipeline.

The version of the pipeline.

An Amazon SageMaker processing job that is used to analyze data and evaluate models. For more information, see Process Data and Evaluate Models.

Configuration information for updating the Amazon SageMaker Debugger profile parameters, system and framework metrics configurations, and storage paths.

The properties of a project as returned by the Search API.

Sourcemodule PutModelPackageGroupPolicyInput = Awso_sagemaker.Values.PutModelPackageGroupPolicyInput

Adds a resouce policy to control access to a model group. For information about resoure policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..

Sourcemodule PutModelPackageGroupPolicyOutput = Awso_sagemaker.Values.PutModelPackageGroupPolicyOutput

Adds a resouce policy to control access to a model group. For information about resoure policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..

A set of filters to narrow the set of lineage entities connected to the StartArn(s) returned by the QueryLineage API action.

Use this action to inspect your lineage and discover relationships between entities. For more information, see Querying Lineage Entities in the Amazon SageMaker Developer Guide.

A lineage entity connected to the starting entity(ies).

Use this action to inspect your lineage and discover relationships between entities. For more information, see Querying Lineage Entities in the Amazon SageMaker Developer Guide.

Register devices.

Sourcemodule RemoteDebugConfigForUpdate = Awso_sagemaker.Values.RemoteDebugConfigForUpdate

Configuration for remote debugging for the UpdateTrainingJob API. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.

Contains input values for a task.

Renders the UI template so that you can preview the worker's experience.

A description of an error that occurred while rendering the template.

Renders the UI template so that you can preview the worker's experience.

Sourcemodule ReservedCapacityInstanceCount = Awso_sagemaker.Values.ReservedCapacityInstanceCount

Details about a reserved capacity offering for a training plan offering. For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see CreateTrainingPlan .

The ResourceConfig to update KeepAlivePeriodInSeconds. Other fields in the ResourceConfig cannot be updated.

Sourcemodule RetryPipelineExecutionRequest = Awso_sagemaker.Values.RetryPipelineExecutionRequest

Retry the execution of the pipeline.

Sourcemodule RetryPipelineExecutionResponse = Awso_sagemaker.Values.RetryPipelineExecutionResponse

Retry the execution of the pipeline.

module SearchExpression = Awso_sagemaker.Values.SearchExpression

A multi-expression that searches for the specified resource or resources in a search. All resource objects that satisfy the expression's condition are included in the search results. You must specify at least one subexpression, filter, or nested filter. A SearchExpression can contain up to twenty elements. A SearchExpression contains the following components: A list of Filter objects. Each filter defines a simple Boolean expression comprised of a resource property name, Boolean operator, and value. A list of NestedFilter objects. Each nested filter defines a list of Boolean expressions using a list of resource properties. A nested filter is satisfied if a single object in the list satisfies all Boolean expressions. A list of SearchExpression objects. A search expression object can be nested in a list of search expression objects. A Boolean operator: And or Or.

module SearchExpressionList = Awso_sagemaker.Values.SearchExpressionList

Contains information about a training job.

Sourcemodule TrialComponentSourceDetail = Awso_sagemaker.Values.TrialComponentSourceDetail

Detailed information about the source of a trial component. Either ProcessingJob or TrainingJob is returned.

The properties of a trial component as returned by the Search API.

Sourcemodule TrialComponentSimpleSummary = Awso_sagemaker.Values.TrialComponentSimpleSummary

A short summary of a trial component.

Sourcemodule TrialComponentSimpleSummaries = Awso_sagemaker.Values.TrialComponentSimpleSummaries

The properties of a trial as returned by the Search API.

A single resource returned as part of the Search API response.

The list of key-value pairs used to filter your search results. If a search result contains a key from your list, it is included in the final search response if the value associated with the key in the result matches the value you specified. If the value doesn't match, the result is excluded from the search response. Any resources that don't have a key from the list that you've provided will also be included in the search response.

Finds SageMaker resources that match a search query. Matching resources are returned as a list of SearchRecord objects in the response. You can sort the search results by any resource property in a ascending or descending order. You can query against the following value types: numeric, text, Boolean, and timestamp. The Search API may provide access to otherwise restricted data. See Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference for more information.

Represents the total number of matching results and indicates how accurate that count is. The Value field provides the count, which may be exact or estimated. The Relation field indicates whether it's an exact figure or a lower bound. This helps understand the full scope of search results, especially when dealing with large result sets.

Finds SageMaker resources that match a search query. Matching resources are returned as a list of SearchRecord objects in the response. You can sort the search results by any resource property in a ascending or descending order. You can query against the following value types: numeric, text, Boolean, and timestamp. The Search API may provide access to otherwise restricted data. See Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference for more information.

Sourcemodule TrainingPlanDurationHoursInput = Awso_sagemaker.Values.TrainingPlanDurationHoursInput
Sourcemodule SearchTrainingPlanOfferingsRequest = Awso_sagemaker.Values.SearchTrainingPlanOfferingsRequest

Searches for available training plan offerings based on specified criteria. Users search for available plan offerings based on their requirements (e.g., instance type, count, start time, duration). And then, they create a plan that best matches their needs using the ID of the plan offering they want to use. For more information about how to reserve GPU capacity for your SageMaker training jobs or SageMaker HyperPod clusters using Amazon SageMaker Training Plan , see CreateTrainingPlan .

Details about a training plan offering. For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see CreateTrainingPlan .

Sourcemodule TrainingPlanExtensionOffering = Awso_sagemaker.Values.TrainingPlanExtensionOffering

Details about an available extension offering for a training plan. Use the offering ID with the ExtendTrainingPlan API to extend a training plan.

Sourcemodule TrainingPlanExtensionOfferings = Awso_sagemaker.Values.TrainingPlanExtensionOfferings
Sourcemodule SearchTrainingPlanOfferingsResponse = Awso_sagemaker.Values.SearchTrainingPlanOfferingsResponse

Searches for available training plan offerings based on specified criteria. Users search for available plan offerings based on their requirements (e.g., instance type, count, start time, duration). And then, they create a plan that best matches their needs using the ID of the plan offering they want to use. For more information about how to reserve GPU capacity for your SageMaker training jobs or SageMaker HyperPod clusters using Amazon SageMaker Training Plan , see CreateTrainingPlan .

Sourcemodule SendPipelineExecutionStepFailureRequest = Awso_sagemaker.Values.SendPipelineExecutionStepFailureRequest

Notifies the pipeline that the execution of a callback step failed, along with a message describing why. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).

Sourcemodule SendPipelineExecutionStepFailureResponse = Awso_sagemaker.Values.SendPipelineExecutionStepFailureResponse

Notifies the pipeline that the execution of a callback step failed, along with a message describing why. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).

Sourcemodule SendPipelineExecutionStepSuccessRequest = Awso_sagemaker.Values.SendPipelineExecutionStepSuccessRequest

Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).

Sourcemodule SendPipelineExecutionStepSuccessResponse = Awso_sagemaker.Values.SendPipelineExecutionStepSuccessResponse

Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).

Sourcemodule ServiceCatalogProvisioningUpdateDetails = Awso_sagemaker.Values.ServiceCatalogProvisioningUpdateDetails

Details that you specify to provision a service catalog product. For information about service catalog, see What is Amazon Web Services Service Catalog.

Sourcemodule StartClusterHealthCheckRequest = Awso_sagemaker.Values.StartClusterHealthCheckRequest

Start deep health checks for a SageMaker HyperPod cluster. You can use DescribeClusterNode API to track progress of the deep health checks. The unhealthy nodes will be automatically rebooted or replaced. Please see Resilience-related Kubernetes labels by SageMaker HyperPod for details.

Sourcemodule StartClusterHealthCheckResponse = Awso_sagemaker.Values.StartClusterHealthCheckResponse

Start deep health checks for a SageMaker HyperPod cluster. You can use DescribeClusterNode API to track progress of the deep health checks. The unhealthy nodes will be automatically rebooted or replaced. Please see Resilience-related Kubernetes labels by SageMaker HyperPod for details.

Sourcemodule StartEdgeDeploymentStageRequest = Awso_sagemaker.Values.StartEdgeDeploymentStageRequest

Starts a stage in an edge deployment plan.

Sourcemodule StartInferenceExperimentRequest = Awso_sagemaker.Values.StartInferenceExperimentRequest

Starts an inference experiment.

Sourcemodule StartInferenceExperimentResponse = Awso_sagemaker.Values.StartInferenceExperimentResponse

Starts an inference experiment.

Sourcemodule StartMlflowTrackingServerRequest = Awso_sagemaker.Values.StartMlflowTrackingServerRequest

Programmatically start an MLflow Tracking Server.

Sourcemodule StartMlflowTrackingServerResponse = Awso_sagemaker.Values.StartMlflowTrackingServerResponse

Programmatically start an MLflow Tracking Server.

Sourcemodule StartMonitoringScheduleRequest = Awso_sagemaker.Values.StartMonitoringScheduleRequest

Starts a previously stopped monitoring schedule. By default, when you successfully create a new schedule, the status of a monitoring schedule is scheduled.

Sourcemodule StartNotebookInstanceInput = Awso_sagemaker.Values.StartNotebookInstanceInput

Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume. After configuring the notebook instance, SageMaker AI sets the notebook instance status to InService. A notebook instance's status must be InService before you can connect to your Jupyter notebook.

Sourcemodule StartPipelineExecutionRequest = Awso_sagemaker.Values.StartPipelineExecutionRequest

Starts a pipeline execution.

Sourcemodule StartPipelineExecutionResponse = Awso_sagemaker.Values.StartPipelineExecutionResponse

Starts a pipeline execution.

Initiates a remote connection session between a local integrated development environments (IDEs) and a remote SageMaker space.

Initiates a remote connection session between a local integrated development environments (IDEs) and a remote SageMaker space.

Stops a running AI benchmark job.

Sourcemodule StopAIBenchmarkJobResponse = Awso_sagemaker.Values.StopAIBenchmarkJobResponse

Stops a running AI benchmark job.

Sourcemodule StopAIRecommendationJobRequest = Awso_sagemaker.Values.StopAIRecommendationJobRequest

Stops a running AI recommendation job.

Sourcemodule StopAIRecommendationJobResponse = Awso_sagemaker.Values.StopAIRecommendationJobResponse

Stops a running AI recommendation job.

A method for forcing a running job to shut down.

Stops a model compilation job. To stop a job, Amazon SageMaker AI sends the algorithm the SIGTERM signal. This gracefully shuts the job down. If the job hasn't stopped, it sends the SIGKILL signal. When it receives a StopCompilationJob request, Amazon SageMaker AI changes the CompilationJobStatus of the job to Stopping. After Amazon SageMaker stops the job, it sets the CompilationJobStatus to Stopped.

Sourcemodule StopEdgeDeploymentStageRequest = Awso_sagemaker.Values.StopEdgeDeploymentStageRequest

Stops a stage in an edge deployment plan.

Sourcemodule StopEdgePackagingJobRequest = Awso_sagemaker.Values.StopEdgePackagingJobRequest

Request to stop an edge packaging job.

Sourcemodule StopHyperParameterTuningJobRequest = Awso_sagemaker.Values.StopHyperParameterTuningJobRequest

Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched. All model artifacts output from the training jobs are stored in Amazon Simple Storage Service (Amazon S3). All data that the training jobs write to Amazon CloudWatch Logs are still available in CloudWatch. After the tuning job moves to the Stopped state, it releases all reserved resources for the tuning job.

Sourcemodule StopInferenceExperimentRequest = Awso_sagemaker.Values.StopInferenceExperimentRequest

Stops an inference experiment.

Sourcemodule StopInferenceExperimentResponse = Awso_sagemaker.Values.StopInferenceExperimentResponse

Stops an inference experiment.

Sourcemodule StopInferenceRecommendationsJobRequest = Awso_sagemaker.Values.StopInferenceRecommendationsJobRequest

Stops an Inference Recommender job.

Stops a running labeling job. A job that is stopped cannot be restarted. Any results obtained before the job is stopped are placed in the Amazon S3 output bucket.

Sourcemodule StopMlflowTrackingServerRequest = Awso_sagemaker.Values.StopMlflowTrackingServerRequest

Programmatically stop an MLflow Tracking Server.

Sourcemodule StopMlflowTrackingServerResponse = Awso_sagemaker.Values.StopMlflowTrackingServerResponse

Programmatically stop an MLflow Tracking Server.

Sourcemodule StopMonitoringScheduleRequest = Awso_sagemaker.Values.StopMonitoringScheduleRequest

Stops a previously started monitoring schedule.

Terminates the ML compute instance. Before terminating the instance, SageMaker AI disconnects the ML storage volume from it. SageMaker AI preserves the ML storage volume. SageMaker AI stops charging you for the ML compute instance when you call StopNotebookInstance. To access data on the ML storage volume for a notebook instance that has been terminated, call the StartNotebookInstance API. StartNotebookInstance launches another ML compute instance, configures it, and attaches the preserved ML storage volume so you can continue your work.

Sourcemodule StopOptimizationJobRequest = Awso_sagemaker.Values.StopOptimizationJobRequest

Ends a running inference optimization job.

Sourcemodule StopPipelineExecutionRequest = Awso_sagemaker.Values.StopPipelineExecutionRequest

Stops a pipeline execution. Callback Step A pipeline execution won't stop while a callback step is running. When you call StopPipelineExecution on a pipeline execution with a running callback step, SageMaker Pipelines sends an additional Amazon SQS message to the specified SQS queue. The body of the SQS message contains a "Status" field which is set to "Stopping". You should add logic to your Amazon SQS message consumer to take any needed action (for example, resource cleanup) upon receipt of the message followed by a call to SendPipelineExecutionStepSuccess or SendPipelineExecutionStepFailure. Only when SageMaker Pipelines receives one of these calls will it stop the pipeline execution. Lambda Step A pipeline execution can't be stopped while a lambda step is running because the Lambda function invoked by the lambda step can't be stopped. If you attempt to stop the execution while the Lambda function is running, the pipeline waits for the Lambda function to finish or until the timeout is hit, whichever occurs first, and then stops. If the Lambda function finishes, the pipeline execution status is Stopped. If the timeout is hit the pipeline execution status is Failed.

Sourcemodule StopPipelineExecutionResponse = Awso_sagemaker.Values.StopPipelineExecutionResponse

Stops a pipeline execution. Callback Step A pipeline execution won't stop while a callback step is running. When you call StopPipelineExecution on a pipeline execution with a running callback step, SageMaker Pipelines sends an additional Amazon SQS message to the specified SQS queue. The body of the SQS message contains a "Status" field which is set to "Stopping". You should add logic to your Amazon SQS message consumer to take any needed action (for example, resource cleanup) upon receipt of the message followed by a call to SendPipelineExecutionStepSuccess or SendPipelineExecutionStepFailure. Only when SageMaker Pipelines receives one of these calls will it stop the pipeline execution. Lambda Step A pipeline execution can't be stopped while a lambda step is running because the Lambda function invoked by the lambda step can't be stopped. If you attempt to stop the execution while the Lambda function is running, the pipeline waits for the Lambda function to finish or until the timeout is hit, whichever occurs first, and then stops. If the Lambda function finishes, the pipeline execution status is Stopped. If the timeout is hit the pipeline execution status is Failed.

Stops a processing job.

Stops a training job. To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of the training is not lost. When it receives a StopTrainingJob request, SageMaker changes the status of the job to Stopping. After SageMaker stops the job, it sets the status to Stopped.

Stops a batch transform job. When Amazon SageMaker receives a StopTransformJob request, the status of the job changes to Stopping. After Amazon SageMaker stops the job, the status is set to Stopped. When you stop a batch transform job before it is completed, Amazon SageMaker doesn't store the job's output in Amazon S3.

The new throughput configuration for the feature group. You can switch between on-demand and provisioned modes or update the read / write capacity of provisioned feature groups. You can switch a feature group to on-demand only once in a 24 hour period.

Updates an action.

Updates an action.

Sourcemodule UpdateAppImageConfigRequest = Awso_sagemaker.Values.UpdateAppImageConfigRequest

Updates the properties of an AppImageConfig.

Sourcemodule UpdateAppImageConfigResponse = Awso_sagemaker.Values.UpdateAppImageConfigResponse

Updates the properties of an AppImageConfig.

Updates an artifact.

Updates an artifact.

Updates a SageMaker HyperPod cluster.

Updates a SageMaker HyperPod cluster.

Sourcemodule UpdateClusterSchedulerConfigRequest = Awso_sagemaker.Values.UpdateClusterSchedulerConfigRequest

Update the cluster policy configuration.

Sourcemodule UpdateClusterSchedulerConfigResponse = Awso_sagemaker.Values.UpdateClusterSchedulerConfigResponse

Update the cluster policy configuration.

Sourcemodule UpdateClusterSoftwareInstanceGroupSpecification = Awso_sagemaker.Values.UpdateClusterSoftwareInstanceGroupSpecification

The configuration that describes specifications of the instance groups to update.

Sourcemodule UpdateClusterSoftwareInstanceGroups = Awso_sagemaker.Values.UpdateClusterSoftwareInstanceGroups
Sourcemodule UpdateClusterSoftwareRequest = Awso_sagemaker.Values.UpdateClusterSoftwareRequest

Updates the platform software of a SageMaker HyperPod cluster for security patching. To learn how to use this API, see Update the SageMaker HyperPod platform software of a cluster. The UpgradeClusterSoftware API call may impact your SageMaker HyperPod cluster uptime and availability. Plan accordingly to mitigate potential disruptions to your workloads.

Sourcemodule UpdateClusterSoftwareResponse = Awso_sagemaker.Values.UpdateClusterSoftwareResponse

Updates the platform software of a SageMaker HyperPod cluster for security patching. To learn how to use this API, see Update the SageMaker HyperPod platform software of a cluster. The UpgradeClusterSoftware API call may impact your SageMaker HyperPod cluster uptime and availability. Plan accordingly to mitigate potential disruptions to your workloads.

Updates the specified Git repository with the specified values.

Sourcemodule UpdateCodeRepositoryOutput = Awso_sagemaker.Values.UpdateCodeRepositoryOutput

Updates the specified Git repository with the specified values.

Update the compute allocation definition.

Sourcemodule UpdateComputeQuotaResponse = Awso_sagemaker.Values.UpdateComputeQuotaResponse

Update the compute allocation definition.

Updates a context.

Updates a context.

Updates a fleet of devices.

Updates one or more devices in a fleet.

Updates the default settings for new user profiles in the domain.

Updates the default settings for new user profiles in the domain.

Specifies a production variant property type for an Endpoint. If you are updating an endpoint with the RetainAllVariantProperties option of UpdateEndpointInput set to true, the VariantProperty objects listed in the ExcludeRetainedVariantProperties parameter of UpdateEndpointInput override the existing variant properties of the endpoint.

Deploys the EndpointConfig specified in the request to a new fleet of instances. SageMaker shifts endpoint traffic to the new instances with the updated endpoint configuration and then deletes the old instances using the previous EndpointConfig (there is no availability loss). For more information about how to control the update and traffic shifting process, see Update models in production. When SageMaker receives the request, it sets the endpoint status to Updating. After updating the endpoint, it sets the status to InService. To check the status of an endpoint, use the DescribeEndpoint API. You must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig. If you delete the EndpointConfig of an endpoint that is active or being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges.

Deploys the EndpointConfig specified in the request to a new fleet of instances. SageMaker shifts endpoint traffic to the new instances with the updated endpoint configuration and then deletes the old instances using the previous EndpointConfig (there is no availability loss). For more information about how to control the update and traffic shifting process, see Update models in production. When SageMaker receives the request, it sets the endpoint status to Updating. After updating the endpoint, it sets the status to InService. To check the status of an endpoint, use the DescribeEndpoint API. You must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig. If you delete the EndpointConfig of an endpoint that is active or being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges.

Sourcemodule UpdateEndpointWeightsAndCapacitiesInput = Awso_sagemaker.Values.UpdateEndpointWeightsAndCapacitiesInput

Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint. When it receives the request, SageMaker sets the endpoint status to Updating. After updating the endpoint, it sets the status to InService. To check the status of an endpoint, use the DescribeEndpoint API.

Sourcemodule UpdateEndpointWeightsAndCapacitiesOutput = Awso_sagemaker.Values.UpdateEndpointWeightsAndCapacitiesOutput

Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint. When it receives the request, SageMaker sets the endpoint status to Updating. After updating the endpoint, it sets the status to InService. To check the status of an endpoint, use the DescribeEndpoint API.

Adds, updates, or removes the description of an experiment. Updates the display name of an experiment.

Adds, updates, or removes the description of an experiment. Updates the display name of an experiment.

Updates the feature group by either adding features or updating the online store configuration. Use one of the following request parameters at a time while using the UpdateFeatureGroup API. You can add features for your feature group using the FeatureAdditions request parameter. Features cannot be removed from a feature group. You can update the online store configuration by using the OnlineStoreConfig request parameter. If a TtlDuration is specified, the default TtlDuration applies for all records added to the feature group after the feature group is updated. If a record level TtlDuration exists from using the PutRecord API, the record level TtlDuration applies to that record instead of the default TtlDuration. To remove the default TtlDuration from an existing feature group, use the UpdateFeatureGroup API and set the TtlDuration Unit and Value to null.

Sourcemodule UpdateFeatureGroupResponse = Awso_sagemaker.Values.UpdateFeatureGroupResponse

Updates the feature group by either adding features or updating the online store configuration. Use one of the following request parameters at a time while using the UpdateFeatureGroup API. You can add features for your feature group using the FeatureAdditions request parameter. Features cannot be removed from a feature group. You can update the online store configuration by using the OnlineStoreConfig request parameter. If a TtlDuration is specified, the default TtlDuration applies for all records added to the feature group after the feature group is updated. If a record level TtlDuration exists from using the PutRecord API, the record level TtlDuration applies to that record instead of the default TtlDuration. To remove the default TtlDuration from an existing feature group, use the UpdateFeatureGroup API and set the TtlDuration Unit and Value to null.

Sourcemodule UpdateFeatureMetadataRequest = Awso_sagemaker.Values.UpdateFeatureMetadataRequest

Updates the description and parameters of the feature group.

Sourcemodule UpdateHubContentReferenceRequest = Awso_sagemaker.Values.UpdateHubContentReferenceRequest

Updates the contents of a SageMaker hub for a ModelReference resource. A ModelReference allows you to access public SageMaker JumpStart models from within your private hub. When using this API, you can update the MinVersion field for additional flexibility in the model version. You shouldn't update any additional fields when using this API, because the metadata in your private hub should match the public JumpStart model's metadata. If you want to update a Model or Notebook resource in your hub, use the UpdateHubContent API instead. For more information about adding model references to your hub, see Add models to a private hub.

Sourcemodule UpdateHubContentReferenceResponse = Awso_sagemaker.Values.UpdateHubContentReferenceResponse

Updates the contents of a SageMaker hub for a ModelReference resource. A ModelReference allows you to access public SageMaker JumpStart models from within your private hub. When using this API, you can update the MinVersion field for additional flexibility in the model version. You shouldn't update any additional fields when using this API, because the metadata in your private hub should match the public JumpStart model's metadata. If you want to update a Model or Notebook resource in your hub, use the UpdateHubContent API instead. For more information about adding model references to your hub, see Add models to a private hub.

Updates SageMaker hub content (either a Model or Notebook resource). You can update the metadata that describes the resource. In addition to the required request fields, specify at least one of the following fields to update: HubContentDescription HubContentDisplayName HubContentMarkdown HubContentSearchKeywords SupportStatus For more information about hubs, see Private curated hubs for foundation model access control in JumpStart. If you want to update a ModelReference resource in your hub, use the UpdateHubContentResource API instead.

Updates SageMaker hub content (either a Model or Notebook resource). You can update the metadata that describes the resource. In addition to the required request fields, specify at least one of the following fields to update: HubContentDescription HubContentDisplayName HubContentMarkdown HubContentSearchKeywords SupportStatus For more information about hubs, see Private curated hubs for foundation model access control in JumpStart. If you want to update a ModelReference resource in your hub, use the UpdateHubContentResource API instead.

Update a hub.

Update a hub.

Updates the properties of a SageMaker AI image. To change the image's tags, use the AddTags and DeleteTags APIs.

Updates the properties of a SageMaker AI image. To change the image's tags, use the AddTags and DeleteTags APIs.

Updates the properties of a SageMaker AI image version.

Sourcemodule UpdateImageVersionResponse = Awso_sagemaker.Values.UpdateImageVersionResponse

Updates the properties of a SageMaker AI image version.

Sourcemodule UpdateInferenceComponentInput = Awso_sagemaker.Values.UpdateInferenceComponentInput

Updates an inference component.

Sourcemodule UpdateInferenceComponentOutput = Awso_sagemaker.Values.UpdateInferenceComponentOutput

Updates an inference component.

Sourcemodule UpdateInferenceComponentRuntimeConfigInput = Awso_sagemaker.Values.UpdateInferenceComponentRuntimeConfigInput

Runtime settings for a model that is deployed with an inference component.

Sourcemodule UpdateInferenceComponentRuntimeConfigOutput = Awso_sagemaker.Values.UpdateInferenceComponentRuntimeConfigOutput

Runtime settings for a model that is deployed with an inference component.

Sourcemodule UpdateInferenceExperimentRequest = Awso_sagemaker.Values.UpdateInferenceExperimentRequest

Updates an inference experiment that you created. The status of the inference experiment has to be either Created, Running. For more information on the status of an inference experiment, see DescribeInferenceExperiment.

Sourcemodule UpdateInferenceExperimentResponse = Awso_sagemaker.Values.UpdateInferenceExperimentResponse

Updates an inference experiment that you created. The status of the inference experiment has to be either Created, Running. For more information on the status of an inference experiment, see DescribeInferenceExperiment.

Updates an MLflow App.

Updates an MLflow App.

Sourcemodule UpdateMlflowTrackingServerRequest = Awso_sagemaker.Values.UpdateMlflowTrackingServerRequest

Updates properties of an existing MLflow Tracking Server.

Sourcemodule UpdateMlflowTrackingServerResponse = Awso_sagemaker.Values.UpdateMlflowTrackingServerResponse

Updates properties of an existing MLflow Tracking Server.

Update an Amazon SageMaker Model Card. You cannot update both model card content and model card status in a single call.

Update an Amazon SageMaker Model Card. You cannot update both model card content and model card status in a single call.

Updates a versioned model.

Updates a versioned model.

Sourcemodule UpdateMonitoringAlertRequest = Awso_sagemaker.Values.UpdateMonitoringAlertRequest

Update the parameters of a model monitor alert.

Sourcemodule UpdateMonitoringAlertResponse = Awso_sagemaker.Values.UpdateMonitoringAlertResponse

Update the parameters of a model monitor alert.

Sourcemodule UpdateMonitoringScheduleRequest = Awso_sagemaker.Values.UpdateMonitoringScheduleRequest

Updates a previously created schedule.

Sourcemodule UpdateMonitoringScheduleResponse = Awso_sagemaker.Values.UpdateMonitoringScheduleResponse

Updates a previously created schedule.

Sourcemodule UpdateNotebookInstanceInput = Awso_sagemaker.Values.UpdateNotebookInstanceInput

Updates a notebook instance. NotebookInstance updates include upgrading or downgrading the ML compute instance used for your notebook instance to accommodate changes in your workload requirements. This API can attach lifecycle configurations to notebook instances. Lifecycle configuration scripts execute with root access and the notebook instance's IAM execution role privileges. Principals with this permission and access to lifecycle configurations can execute code with the execution role's credentials. See Customize a Notebook Instance Using a Lifecycle Configuration Script for security best practices.

Sourcemodule UpdateNotebookInstanceLifecycleConfigInput = Awso_sagemaker.Values.UpdateNotebookInstanceLifecycleConfigInput

Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API. Updates to lifecycle configurations affect all notebook instances using that configuration upon their next start. Lifecycle configuration scripts execute with root access and the notebook instance's IAM execution role privileges. Grant this permission only to trusted principals. See Customize a Notebook Instance Using a Lifecycle Configuration Script for security best practices.

Sourcemodule UpdateNotebookInstanceLifecycleConfigOutput = Awso_sagemaker.Values.UpdateNotebookInstanceLifecycleConfigOutput

Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API. Updates to lifecycle configurations affect all notebook instances using that configuration upon their next start. Lifecycle configuration scripts execute with root access and the notebook instance's IAM execution role privileges. Grant this permission only to trusted principals. See Customize a Notebook Instance Using a Lifecycle Configuration Script for security best practices.

Sourcemodule UpdateNotebookInstanceOutput = Awso_sagemaker.Values.UpdateNotebookInstanceOutput

Updates a notebook instance. NotebookInstance updates include upgrading or downgrading the ML compute instance used for your notebook instance to accommodate changes in your workload requirements. This API can attach lifecycle configurations to notebook instances. Lifecycle configuration scripts execute with root access and the notebook instance's IAM execution role privileges. Principals with this permission and access to lifecycle configurations can execute code with the execution role's credentials. See Customize a Notebook Instance Using a Lifecycle Configuration Script for security best practices.

Updates all of the SageMaker Partner AI Apps in an account.

Updates all of the SageMaker Partner AI Apps in an account.

Sourcemodule UpdatePipelineExecutionRequest = Awso_sagemaker.Values.UpdatePipelineExecutionRequest

Updates a pipeline execution.

Sourcemodule UpdatePipelineExecutionResponse = Awso_sagemaker.Values.UpdatePipelineExecutionResponse

Updates a pipeline execution.

Updates a pipeline.

Updates a pipeline.

Sourcemodule UpdatePipelineVersionRequest = Awso_sagemaker.Values.UpdatePipelineVersionRequest

Updates a pipeline version.

Sourcemodule UpdatePipelineVersionResponse = Awso_sagemaker.Values.UpdatePipelineVersionResponse

Updates a pipeline version.

Contains configuration details for updating an existing template provider in the project.

Sourcemodule UpdateTemplateProviderList = Awso_sagemaker.Values.UpdateTemplateProviderList

Updates a machine learning (ML) project that is created from a template that sets up an ML pipeline from training to deploying an approved model. You must not update a project that is in use. If you update the ServiceCatalogProvisioningUpdateDetails of a project that is active or being created, or updated, you may lose resources already created by the project.

Updates a machine learning (ML) project that is created from a template that sets up an ML pipeline from training to deploying an approved model. You must not update a project that is in use. If you update the ServiceCatalogProvisioningUpdateDetails of a project that is active or being created, or updated, you may lose resources already created by the project.

Updates the settings of a space. You can't edit the app type of a space in the SpaceSettings.

Updates the settings of a space. You can't edit the app type of a space in the SpaceSettings.

Update a model training job to request a new Debugger profiling configuration or to change warm pool retention length.

Update a model training job to request a new Debugger profiling configuration or to change warm pool retention length.

Sourcemodule UpdateTrialComponentRequest = Awso_sagemaker.Values.UpdateTrialComponentRequest

Updates one or more properties of a trial component.

Sourcemodule UpdateTrialComponentResponse = Awso_sagemaker.Values.UpdateTrialComponentResponse

Updates one or more properties of a trial component.

Updates the display name of a trial.

Updates the display name of a trial.

Updates a user profile.

Updates a user profile.

Use this operation to update your workforce. You can use this operation to require that workers use specific IP addresses to work on tasks and to update your OpenID Connect (OIDC) Identity Provider (IdP) workforce configuration. The worker portal is now supported in VPC and public internet. Use SourceIpConfig to restrict worker access to tasks to a specific range of IP addresses. You specify allowed IP addresses by creating a list of up to ten CIDRs. By default, a workforce isn't restricted to specific IP addresses. If you specify a range of IP addresses, workers who attempt to access tasks using any IP address outside the specified range are denied and get a Not Found error message on the worker portal. To restrict public internet access for all workers, configure the SourceIpConfig CIDR value. For example, when using SourceIpConfig with an IpAddressType of IPv4, you can restrict access to the IPv4 CIDR block "10.0.0.0/16". When using an IpAddressType of dualstack, you can specify both the IPv4 and IPv6 CIDR blocks, such as "10.0.0.0/16" for IPv4 only, "2001:db8:1234:1a00::/56" for IPv6 only, or "10.0.0.0/16" and "2001:db8:1234:1a00::/56" for dual stack. Amazon SageMaker does not support Source Ip restriction for worker portals in VPC. Use OidcConfig to update the configuration of a workforce created using your own OIDC IdP. You can only update your OIDC IdP configuration when there are no work teams associated with your workforce. You can delete work teams using the DeleteWorkteam operation. After restricting access to a range of IP addresses or updating your OIDC IdP configuration with this operation, you can view details about your update workforce using the DescribeWorkforce operation. This operation only applies to private workforces.

Use this operation to update your workforce. You can use this operation to require that workers use specific IP addresses to work on tasks and to update your OpenID Connect (OIDC) Identity Provider (IdP) workforce configuration. The worker portal is now supported in VPC and public internet. Use SourceIpConfig to restrict worker access to tasks to a specific range of IP addresses. You specify allowed IP addresses by creating a list of up to ten CIDRs. By default, a workforce isn't restricted to specific IP addresses. If you specify a range of IP addresses, workers who attempt to access tasks using any IP address outside the specified range are denied and get a Not Found error message on the worker portal. To restrict public internet access for all workers, configure the SourceIpConfig CIDR value. For example, when using SourceIpConfig with an IpAddressType of IPv4, you can restrict access to the IPv4 CIDR block "10.0.0.0/16". When using an IpAddressType of dualstack, you can specify both the IPv4 and IPv6 CIDR blocks, such as "10.0.0.0/16" for IPv4 only, "2001:db8:1234:1a00::/56" for IPv6 only, or "10.0.0.0/16" and "2001:db8:1234:1a00::/56" for dual stack. Amazon SageMaker does not support Source Ip restriction for worker portals in VPC. Use OidcConfig to update the configuration of a workforce created using your own OIDC IdP. You can only update your OIDC IdP configuration when there are no work teams associated with your workforce. You can delete work teams using the DeleteWorkteam operation. After restricting access to a range of IP addresses or updating your OIDC IdP configuration with this operation, you can view details about your update workforce using the DescribeWorkforce operation. This operation only applies to private workforces.

Updates an existing work team with new member definitions or description.

Updates an existing work team with new member definitions or description.