SageMaker class

Provides APIs for creating and managing SageMaker resources.

Other Resources:

• SageMaker Developer Guide
• Amazon Augmented AI Runtime API Reference

Constructors

SageMaker({required String region, AwsClientCredentials? credentials, AwsClientCredentialsProvider? credentialsProvider, Client? client, String? endpointUrl})

Properties

hashCode → int

The hash code for this object.

no setter, inherited

runtimeType → Type

A representation of the runtime type of the object.

no setter, inherited

Methods

addAssociation({required String destinationArn, required String sourceArn, AssociationEdgeType? associationType}) → Future<AddAssociationResponse>

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.

addTags({required String resourceArn, required List<Tag> tags}) → Future<AddTagsOutput>

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.

associateTrialComponent({required String trialComponentName, required String trialName}) → Future<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.

attachClusterNodeVolume({required String clusterArn, required String nodeId, required String volumeId}) → Future<AttachClusterNodeVolumeResponse>

Attaches your Amazon Elastic Block Store (Amazon EBS) volume to a node in your EKS orchestrated HyperPod cluster.

batchAddClusterNodes({required String clusterName, required List<AddClusterNodeSpecification> nodesToAdd, String? clientToken}) → Future<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.

batchDeleteClusterNodes({required String clusterName, List<String>? nodeIds, List<String>? nodeLogicalIds}) → Future<BatchDeleteClusterNodesResponse>

Deletes specific nodes within a SageMaker HyperPod cluster. BatchDeleteClusterNodes accepts a cluster name and a list of node IDs.

batchDescribeModelPackage({required List<String> modelPackageArnList}) → Future<BatchDescribeModelPackageOutput>

This action batch describes a list of versioned model packages

batchRebootClusterNodes({required String clusterName, List<String>? nodeIds, List<String>? nodeLogicalIds}) → Future<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.

batchReplaceClusterNodes({required String clusterName, List<String>? nodeIds, List<String>? nodeLogicalIds}) → Future<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.

close() → void

Closes the internal HTTP client if none was provided at creation. If a client was passed as a constructor argument, this becomes a noop.

createAction({required String actionName, required String actionType, required ActionSource source, String? description, MetadataProperties? metadataProperties, Map<String, String>? properties, ActionStatus? status, List<Tag>? tags}) → Future<CreateActionResponse>

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.

createAIBenchmarkJob({required String aIBenchmarkJobName, required String aIWorkloadConfigIdentifier, required AIBenchmarkTarget benchmarkTarget, required AIBenchmarkOutputConfig outputConfig, required String roleArn, AIBenchmarkNetworkConfig? networkConfig, List<Tag>? tags}) → Future<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.

createAIRecommendationJob({required String aIRecommendationJobName, required String aIWorkloadConfigIdentifier, required AIModelSource modelSource, required AIRecommendationOutputConfig outputConfig, required AIRecommendationPerformanceTarget performanceTarget, required String roleArn, AIRecommendationComputeSpec? computeSpec, AIRecommendationInferenceSpecification? inferenceSpecification, bool? optimizeModel, List<Tag>? tags}) → Future<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.

createAIWorkloadConfig({required String aIWorkloadConfigName, AIWorkloadConfigs? aIWorkloadConfigs, AIDatasetConfig? datasetConfig, List<Tag>? tags}) → Future<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.

createAlgorithm({required String algorithmName, required TrainingSpecification trainingSpecification, String? algorithmDescription, bool? certifyForMarketplace, InferenceSpecification? inferenceSpecification, List<Tag>? tags, AlgorithmValidationSpecification? validationSpecification}) → Future<CreateAlgorithmOutput>

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

createApp({required String appName, required AppType appType, required String domainId, bool? recoveryMode, ResourceSpec? resourceSpec, String? spaceName, List<Tag>? tags, String? userProfileName}) → Future<CreateAppResponse>

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.

createAppImageConfig({required String appImageConfigName, CodeEditorAppImageConfig? codeEditorAppImageConfig, JupyterLabAppImageConfig? jupyterLabAppImageConfig, KernelGatewayImageConfig? kernelGatewayImageConfig, List<Tag>? tags}) → Future<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.

createArtifact({required String artifactType, required ArtifactSource source, String? artifactName, MetadataProperties? metadataProperties, Map<String, String>? properties, List<Tag>? tags}) → Future<CreateArtifactResponse>

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.

createAutoMLJob({required String autoMLJobName, required List<AutoMLChannel> inputDataConfig, required AutoMLOutputDataConfig outputDataConfig, required String roleArn, AutoMLJobConfig? autoMLJobConfig, AutoMLJobObjective? autoMLJobObjective, bool? generateCandidateDefinitionsOnly, ModelDeployConfig? modelDeployConfig, ProblemType? problemType, List<Tag>? tags}) → Future<CreateAutoMLJobResponse>

Creates an Autopilot job also referred to as Autopilot experiment or AutoML job.

createAutoMLJobV2({required List<AutoMLJobChannel> autoMLJobInputDataConfig, required String autoMLJobName, required AutoMLProblemTypeConfig autoMLProblemTypeConfig, required AutoMLOutputDataConfig outputDataConfig, required String roleArn, AutoMLComputeConfig? autoMLComputeConfig, AutoMLJobObjective? autoMLJobObjective, AutoMLDataSplitConfig? dataSplitConfig, ModelDeployConfig? modelDeployConfig, AutoMLSecurityConfig? securityConfig, List<Tag>? tags}) → Future<CreateAutoMLJobV2Response>

Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.

createCluster({required String clusterName, ClusterAutoScalingConfig? autoScaling, String? clusterRole, List<ClusterInstanceGroupSpecification>? instanceGroups, ClusterNodeProvisioningMode? nodeProvisioningMode, ClusterNodeRecovery? nodeRecovery, ClusterOrchestrator? orchestrator, List<ClusterRestrictedInstanceGroupSpecification>? restrictedInstanceGroups, ClusterRestrictedInstanceGroupsConfig? restrictedInstanceGroupsConfig, List<Tag>? tags, ClusterTieredStorageConfig? tieredStorageConfig, VpcConfig? vpcConfig}) → Future<CreateClusterResponse>

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.

createClusterSchedulerConfig({required String clusterArn, required String name, required SchedulerConfig schedulerConfig, String? description, List<Tag>? tags}) → Future<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.

createCodeRepository({required String codeRepositoryName, required GitConfig gitConfig, List<Tag>? tags}) → Future<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.

createCompilationJob({required String compilationJobName, required OutputConfig outputConfig, required String roleArn, required StoppingCondition stoppingCondition, InputConfig? inputConfig, String? modelPackageVersionArn, List<Tag>? tags, NeoVpcConfig? vpcConfig}) → Future<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.

createComputeQuota({required String clusterArn, required ComputeQuotaConfig computeQuotaConfig, required ComputeQuotaTarget computeQuotaTarget, required String name, ActivationState? activationState, String? description, List<Tag>? tags}) → Future<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.

createContext({required String contextName, required String contextType, required ContextSource source, String? description, Map<String, String>? properties, List<Tag>? tags}) → Future<CreateContextResponse>

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.

createDataQualityJobDefinition({required DataQualityAppSpecification dataQualityAppSpecification, required DataQualityJobInput dataQualityJobInput, required MonitoringOutputConfig dataQualityJobOutputConfig, required String jobDefinitionName, required MonitoringResources jobResources, required String roleArn, DataQualityBaselineConfig? dataQualityBaselineConfig, MonitoringNetworkConfig? networkConfig, MonitoringStoppingCondition? stoppingCondition, List<Tag>? tags}) → Future<CreateDataQualityJobDefinitionResponse>

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

createDeviceFleet({required String deviceFleetName, required EdgeOutputConfig outputConfig, String? description, bool? enableIotRoleAlias, String? roleArn, List<Tag>? tags}) → Future<void>

Creates a device fleet.

createDomain({required AuthMode authMode, required UserSettings defaultUserSettings, required String domainName, AppNetworkAccessType? appNetworkAccessType, AppSecurityGroupManagement? appSecurityGroupManagement, DefaultSpaceSettings? defaultSpaceSettings, DomainSettings? domainSettings, HomeEfsFileSystemCreation? homeEfsFileSystemCreation, String? homeEfsFileSystemKmsKeyId, String? kmsKeyId, List<String>? subnetIds, TagPropagation? tagPropagation, List<Tag>? tags, String? vpcId}) → Future<CreateDomainResponse>

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.

createEdgeDeploymentPlan({required String deviceFleetName, required String edgeDeploymentPlanName, required List<EdgeDeploymentModelConfig> modelConfigs, List<DeploymentStage>? stages, List<Tag>? tags}) → Future<CreateEdgeDeploymentPlanResponse>

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

createEdgeDeploymentStage({required String edgeDeploymentPlanName, required List<DeploymentStage> stages}) → Future<void>

Creates a new stage in an existing edge deployment plan.

createEdgePackagingJob({required String compilationJobName, required String edgePackagingJobName, required String modelName, required String modelVersion, required EdgeOutputConfig outputConfig, required String roleArn, String? resourceKey, List<Tag>? tags}) → Future<void>

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.

createEndpoint({required String endpointConfigName, required String endpointName, DeploymentConfig? deploymentConfig, List<Tag>? tags}) → Future<CreateEndpointOutput>

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.

createEndpointConfig({required String endpointConfigName, required List<ProductionVariant> productionVariants, AsyncInferenceConfig? asyncInferenceConfig, DataCaptureConfig? dataCaptureConfig, bool? enableNetworkIsolation, String? executionRoleArn, ExplainerConfig? explainerConfig, String? kmsKeyId, MetricsConfig? metricsConfig, List<ProductionVariant>? shadowProductionVariants, List<Tag>? tags, VpcConfig? vpcConfig}) → Future<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. 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.

createExperiment({required String experimentName, String? description, String? displayName, List<Tag>? tags}) → Future<CreateExperimentResponse>

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. 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.

createFeatureGroup({required String eventTimeFeatureName, required List<FeatureDefinition> featureDefinitions, required String featureGroupName, required String recordIdentifierFeatureName, String? description, OfflineStoreConfig? offlineStoreConfig, OnlineStoreConfig? onlineStoreConfig, String? roleArn, List<Tag>? tags, ThroughputConfig? throughputConfig}) → Future<CreateFeatureGroupResponse>

Create a new FeatureGroup. A FeatureGroup is a group of Features defined in the FeatureStore to describe a Record.

createFlowDefinition({required String flowDefinitionName, required FlowDefinitionOutputConfig outputConfig, required String roleArn, HumanLoopActivationConfig? humanLoopActivationConfig, HumanLoopConfig? humanLoopConfig, HumanLoopRequestSource? humanLoopRequestSource, List<Tag>? tags}) → Future<CreateFlowDefinitionResponse>

Creates a flow definition.

createHub({required String hubDescription, required String hubName, String? hubDisplayName, List<String>? hubSearchKeywords, HubS3StorageConfig? s3StorageConfig, List<Tag>? tags}) → Future<CreateHubResponse>

Create a hub.

createHubContentPresignedUrls({required String hubContentName, required HubContentType hubContentType, required String hubName, PresignedUrlAccessConfig? accessConfig, String? hubContentVersion, int? maxResults, String? nextToken}) → Future<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.

createHubContentReference({required String hubName, required String sageMakerPublicHubContentArn, String? hubContentName, String? minVersion, List<Tag>? tags}) → Future<CreateHubContentReferenceResponse>

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

createHumanTaskUi({required String humanTaskUiName, required UiTemplate uiTemplate, List<Tag>? tags}) → Future<CreateHumanTaskUiResponse>

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.

createHyperParameterTuningJob({required HyperParameterTuningJobConfig hyperParameterTuningJobConfig, required String hyperParameterTuningJobName, Autotune? autotune, List<Tag>? tags, HyperParameterTrainingJobDefinition? trainingJobDefinition, List<HyperParameterTrainingJobDefinition>? trainingJobDefinitions, HyperParameterTuningJobWarmStartConfig? warmStartConfig}) → Future<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.

createImage({required String imageName, required String roleArn, String? description, String? displayName, List<Tag>? tags}) → Future<CreateImageResponse>

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.

createImageVersion({required String baseImage, required String imageName, List<String>? aliases, String? clientToken, bool? horovod, JobType? jobType, String? mLFramework, Processor? processor, String? programmingLang, String? releaseNotes, VendorGuidance? vendorGuidance}) → Future<CreateImageVersionResponse>

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

createInferenceComponent({required String endpointName, required String inferenceComponentName, InferenceComponentRuntimeConfig? runtimeConfig, InferenceComponentSpecification? specification, List<InferenceComponentSpecification>? specifications, List<Tag>? tags, String? variantName}) → Future<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.

createInferenceExperiment({required String endpointName, required List<ModelVariantConfig> modelVariants, required String name, required String roleArn, required ShadowModeConfig shadowModeConfig, required InferenceExperimentType type, InferenceExperimentDataStorageConfig? dataStorageConfig, String? description, String? kmsKey, InferenceExperimentSchedule? schedule, List<Tag>? tags}) → Future<CreateInferenceExperimentResponse>

Creates an inference experiment using the configurations specified in the request.

createInferenceRecommendationsJob({required RecommendationJobInputConfig inputConfig, required String jobName, required RecommendationJobType jobType, required String roleArn, String? jobDescription, RecommendationJobOutputConfig? outputConfig, RecommendationJobStoppingConditions? stoppingConditions, List<Tag>? tags}) → Future<CreateInferenceRecommendationsJobResponse>

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

createJob({required JobCategory jobCategory, required String jobConfigDocument, required String jobConfigSchemaVersion, required String jobName, required String roleArn, List<Tag>? tags}) → Future<CreateJobResponse>

Creates a model customization job in Amazon SageMaker. A job runs a workload based on the job category and configuration you provide. You specify the job category, a schema-versioned configuration document, and an IAM role that grants Amazon SageMaker permission to access resources on your behalf.

createLabelingJob({required HumanTaskConfig humanTaskConfig, required LabelingJobInputConfig inputConfig, required String labelAttributeName, required String labelingJobName, required LabelingJobOutputConfig outputConfig, required String roleArn, String? labelCategoryConfigS3Uri, LabelingJobAlgorithmsConfig? labelingJobAlgorithmsConfig, LabelingJobStoppingConditions? stoppingConditions, List<Tag>? tags}) → Future<CreateLabelingJobResponse>

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.

createMlflowApp({required String artifactStoreUri, required String name, required String roleArn, AccountDefaultStatus? accountDefaultStatus, List<String>? defaultDomainIdList, ModelRegistrationMode? modelRegistrationMode, List<Tag>? tags, String? weeklyMaintenanceWindowStart}) → Future<CreateMlflowAppResponse>

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

createMlflowTrackingServer({required String artifactStoreUri, required String roleArn, required String trackingServerName, bool? automaticModelRegistration, String? mlflowVersion, String? s3BucketOwnerAccountId, bool? s3BucketOwnerVerification, List<Tag>? tags, TrackingServerSize? trackingServerSize, String? weeklyMaintenanceWindowStart}) → Future<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.

createModel({required String modelName, List<ContainerDefinition>? containers, bool? enableNetworkIsolation, String? executionRoleArn, InferenceExecutionConfig? inferenceExecutionConfig, ContainerDefinition? primaryContainer, List<Tag>? tags, VpcConfig? vpcConfig}) → Future<CreateModelOutput>

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.

createModelBiasJobDefinition({required String jobDefinitionName, required MonitoringResources jobResources, required ModelBiasAppSpecification modelBiasAppSpecification, required ModelBiasJobInput modelBiasJobInput, required MonitoringOutputConfig modelBiasJobOutputConfig, required String roleArn, ModelBiasBaselineConfig? modelBiasBaselineConfig, MonitoringNetworkConfig? networkConfig, MonitoringStoppingCondition? stoppingCondition, List<Tag>? tags}) → Future<CreateModelBiasJobDefinitionResponse>

Creates the definition for a model bias job.

createModelCard({required String content, required String modelCardName, required ModelCardStatus modelCardStatus, ModelCardSecurityConfig? securityConfig, List<Tag>? tags}) → Future<CreateModelCardResponse>

Creates an Amazon SageMaker Model Card.

createModelCardExportJob({required String modelCardExportJobName, required String modelCardName, required ModelCardExportOutputConfig outputConfig, int? modelCardVersion}) → Future<CreateModelCardExportJobResponse>

Creates an Amazon SageMaker Model Card export job.

createModelExplainabilityJobDefinition({required String jobDefinitionName, required MonitoringResources jobResources, required ModelExplainabilityAppSpecification modelExplainabilityAppSpecification, required ModelExplainabilityJobInput modelExplainabilityJobInput, required MonitoringOutputConfig modelExplainabilityJobOutputConfig, required String roleArn, ModelExplainabilityBaselineConfig? modelExplainabilityBaselineConfig, MonitoringNetworkConfig? networkConfig, MonitoringStoppingCondition? stoppingCondition, List<Tag>? tags}) → Future<CreateModelExplainabilityJobDefinitionResponse>

Creates the definition for a model explainability job.

createModelPackage({List<AdditionalInferenceSpecificationDefinition>? additionalInferenceSpecifications, bool? certifyForMarketplace, String? clientToken, Map<String, String>? customerMetadataProperties, String? domain, DriftCheckBaselines? driftCheckBaselines, InferenceSpecification? inferenceSpecification, ManagedStorageType? managedStorageType, MetadataProperties? metadataProperties, ModelApprovalStatus? modelApprovalStatus, ModelPackageModelCard? modelCard, ModelLifeCycle? modelLifeCycle, ModelMetrics? modelMetrics, String? modelPackageDescription, String? modelPackageGroupName, String? modelPackageName, ModelPackageRegistrationType? modelPackageRegistrationType, String? samplePayloadUrl, ModelPackageSecurityConfig? securityConfig, SkipModelValidation? skipModelValidation, SourceAlgorithmSpecification? sourceAlgorithmSpecification, String? sourceUri, List<Tag>? tags, String? task, ModelPackageValidationSpecification? validationSpecification}) → Future<CreateModelPackageOutput>

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.

createModelPackageGroup({required String modelPackageGroupName, ManagedConfiguration? managedConfiguration, String? modelPackageGroupDescription, List<Tag>? tags}) → Future<CreateModelPackageGroupOutput>

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

createModelQualityJobDefinition({required String jobDefinitionName, required MonitoringResources jobResources, required ModelQualityAppSpecification modelQualityAppSpecification, required ModelQualityJobInput modelQualityJobInput, required MonitoringOutputConfig modelQualityJobOutputConfig, required String roleArn, ModelQualityBaselineConfig? modelQualityBaselineConfig, MonitoringNetworkConfig? networkConfig, MonitoringStoppingCondition? stoppingCondition, List<Tag>? tags}) → Future<CreateModelQualityJobDefinitionResponse>

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

createMonitoringSchedule({required MonitoringScheduleConfig monitoringScheduleConfig, required String monitoringScheduleName, List<Tag>? tags}) → Future<CreateMonitoringScheduleResponse>

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

createNotebookInstance({required InstanceType instanceType, required String notebookInstanceName, required String roleArn, List<NotebookInstanceAcceleratorType>? acceleratorTypes, List<String>? additionalCodeRepositories, String? defaultCodeRepository, DirectInternetAccess? directInternetAccess, InstanceMetadataServiceConfiguration? instanceMetadataServiceConfiguration, IPAddressType? ipAddressType, String? kmsKeyId, String? lifecycleConfigName, String? platformIdentifier, RootAccess? rootAccess, List<String>? securityGroupIds, String? subnetId, List<Tag>? tags, int? volumeSizeInGB}) → Future<CreateNotebookInstanceOutput>

Creates an SageMaker AI notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook.

createNotebookInstanceLifecycleConfig({required String notebookInstanceLifecycleConfigName, List<NotebookInstanceLifecycleHook>? onCreate, List<NotebookInstanceLifecycleHook>? onStart, List<Tag>? tags}) → Future<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.

createOptimizationJob({required OptimizationJobDeploymentInstanceType deploymentInstanceType, required OptimizationJobModelSource modelSource, required List<OptimizationConfig> optimizationConfigs, required String optimizationJobName, required OptimizationJobOutputConfig outputConfig, required String roleArn, required StoppingCondition stoppingCondition, int? maxInstanceCount, Map<String, String>? optimizationEnvironment, List<Tag>? tags, OptimizationVpcConfig? vpcConfig}) → Future<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.

createPartnerApp({required PartnerAppAuthType authType, required String executionRoleArn, required String name, required String tier, required PartnerAppType type, PartnerAppConfig? applicationConfig, String? clientToken, bool? enableAutoMinorVersionUpgrade, bool? enableIamSessionBasedIdentity, String? kmsKeyId, PartnerAppMaintenanceConfig? maintenanceConfig, List<Tag>? tags}) → Future<CreatePartnerAppResponse>

Creates an Amazon SageMaker Partner AI App.

createPartnerAppPresignedUrl({required String arn, int? expiresInSeconds, int? sessionExpirationDurationInSeconds}) → Future<CreatePartnerAppPresignedUrlResponse>

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

createPipeline({required String pipelineName, required String roleArn, String? clientRequestToken, ParallelismConfiguration? parallelismConfiguration, String? pipelineDefinition, PipelineDefinitionS3Location? pipelineDefinitionS3Location, String? pipelineDescription, String? pipelineDisplayName, List<Tag>? tags}) → Future<CreatePipelineResponse>

Creates a pipeline using a JSON pipeline definition.

createPresignedDomainUrl({required String domainId, required String userProfileName, int? expiresInSeconds, String? landingUri, int? sessionExpirationDurationInSeconds, String? spaceName}) → Future<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.

createPresignedMlflowAppUrl({required String arn, int? expiresInSeconds, int? sessionExpirationDurationInSeconds}) → Future<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.

createPresignedMlflowTrackingServerUrl({required String trackingServerName, int? expiresInSeconds, int? sessionExpirationDurationInSeconds}) → Future<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.

createPresignedNotebookInstanceUrl({required String notebookInstanceName, int? sessionExpirationDurationInSeconds}) → Future<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.

createProcessingJob({required AppSpecification appSpecification, required String processingJobName, required ProcessingResources processingResources, required String roleArn, Map<String, String>? environment, ExperimentConfig? experimentConfig, NetworkConfig? networkConfig, List<ProcessingInput>? processingInputs, ProcessingOutputConfig? processingOutputConfig, ProcessingStoppingCondition? stoppingCondition, List<Tag>? tags}) → Future<CreateProcessingJobResponse>

Creates a processing job.

createProject({required String projectName, String? projectDescription, ServiceCatalogProvisioningDetails? serviceCatalogProvisioningDetails, List<Tag>? tags, List<CreateTemplateProvider>? templateProviders}) → Future<CreateProjectOutput>

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.

createSpace({required String domainId, required String spaceName, OwnershipSettings? ownershipSettings, String? spaceDisplayName, SpaceSettings? spaceSettings, SpaceSharingSettings? spaceSharingSettings, List<Tag>? tags}) → Future<CreateSpaceResponse>

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

createStudioLifecycleConfig({required StudioLifecycleConfigAppType studioLifecycleConfigAppType, required String studioLifecycleConfigContent, required String studioLifecycleConfigName, List<Tag>? tags}) → Future<CreateStudioLifecycleConfigResponse>

Creates a new Amazon SageMaker AI Studio Lifecycle Configuration.

createTrainingJob({required OutputDataConfig outputDataConfig, required String roleArn, required String trainingJobName, AlgorithmSpecification? algorithmSpecification, CheckpointConfig? checkpointConfig, DebugHookConfig? debugHookConfig, List<DebugRuleConfiguration>? debugRuleConfigurations, bool? enableInterContainerTrafficEncryption, bool? enableManagedSpotTraining, bool? enableNetworkIsolation, Map<String, String>? environment, ExperimentConfig? experimentConfig, Map<String, String>? hyperParameters, InfraCheckConfig? infraCheckConfig, List<Channel>? inputDataConfig, MlflowConfig? mlflowConfig, ModelPackageConfig? modelPackageConfig, ProfilerConfig? profilerConfig, List<ProfilerRuleConfiguration>? profilerRuleConfigurations, RemoteDebugConfig? remoteDebugConfig, ResourceConfig? resourceConfig, RetryStrategy? retryStrategy, ServerlessJobConfig? serverlessJobConfig, SessionChainingConfig? sessionChainingConfig, StoppingCondition? stoppingCondition, List<Tag>? tags, TensorBoardOutputConfig? tensorBoardOutputConfig, VpcConfig? vpcConfig}) → Future<CreateTrainingJobResponse>

Starts a model training job. After training completes, SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.

createTrainingPlan({required String trainingPlanName, required String trainingPlanOfferingId, int? spareInstanceCountPerUltraServer, List<Tag>? tags}) → Future<CreateTrainingPlanResponse>

Creates a new training plan in SageMaker to reserve compute capacity.

createTransformJob({required String modelName, required TransformInput transformInput, required String transformJobName, required TransformOutput transformOutput, required TransformResources transformResources, BatchStrategy? batchStrategy, BatchDataCaptureConfig? dataCaptureConfig, DataProcessing? dataProcessing, Map<String, String>? environment, ExperimentConfig? experimentConfig, int? maxConcurrentTransforms, int? maxPayloadInMB, ModelClientConfig? modelClientConfig, List<Tag>? tags}) → Future<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.

createTrial({required String experimentName, required String trialName, String? displayName, MetadataProperties? metadataProperties, List<Tag>? tags}) → Future<CreateTrialResponse>

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.

createTrialComponent({required String trialComponentName, String? displayName, DateTime? endTime, Map<String, TrialComponentArtifact>? inputArtifacts, MetadataProperties? metadataProperties, Map<String, TrialComponentArtifact>? outputArtifacts, Map<String, TrialComponentParameterValue>? parameters, DateTime? startTime, TrialComponentStatus? status, List<Tag>? tags}) → Future<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.

createUserProfile({required String domainId, required String userProfileName, String? singleSignOnUserIdentifier, String? singleSignOnUserValue, List<Tag>? tags, UserSettings? userSettings}) → Future<CreateUserProfileResponse>

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.

createWorkforce({required String workforceName, CognitoConfig? cognitoConfig, WorkforceIpAddressType? ipAddressType, OidcConfig? oidcConfig, SourceIpConfig? sourceIpConfig, List<Tag>? tags, WorkforceVpcConfigRequest? workforceVpcConfig}) → Future<CreateWorkforceResponse>

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.

createWorkteam({required String description, required List<MemberDefinition> memberDefinitions, required String workteamName, NotificationConfiguration? notificationConfiguration, List<Tag>? tags, WorkerAccessConfiguration? workerAccessConfiguration, String? workforceName}) → Future<CreateWorkteamResponse>

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.

deleteAction({required String actionName}) → Future<DeleteActionResponse>

Deletes an action.

deleteAIBenchmarkJob({required String aIBenchmarkJobName}) → Future<DeleteAIBenchmarkJobResponse>

Deletes the specified AI benchmark job.

deleteAIRecommendationJob({required String aIRecommendationJobName}) → Future<DeleteAIRecommendationJobResponse>

Deletes the specified AI recommendation job.

deleteAIWorkloadConfig({required String aIWorkloadConfigName}) → Future<DeleteAIWorkloadConfigResponse>

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

deleteAlgorithm({required String algorithmName}) → Future<void>

Removes the specified algorithm from your account.

deleteApp({required String appName, required AppType appType, required String domainId, String? spaceName, String? userProfileName}) → Future<void>

Used to stop and delete an app.

deleteAppImageConfig({required String appImageConfigName}) → Future<void>

Deletes an AppImageConfig.

deleteArtifact({String? artifactArn, ArtifactSource? source}) → Future<DeleteArtifactResponse>

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

deleteAssociation({required String destinationArn, required String sourceArn}) → Future<DeleteAssociationResponse>

Deletes an association.

deleteCluster({required String clusterName}) → Future<DeleteClusterResponse>

Delete a SageMaker HyperPod cluster.

deleteClusterSchedulerConfig({required String clusterSchedulerConfigId}) → Future<void>

Deletes the cluster policy of the cluster.

deleteCodeRepository({required String codeRepositoryName}) → Future<void>

Deletes the specified Git repository from your account.

deleteCompilationJob({required String compilationJobName}) → Future<void>

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.

deleteComputeQuota({required String computeQuotaId}) → Future<void>

Deletes the compute allocation from the cluster.

deleteContext({required String contextName}) → Future<DeleteContextResponse>

Deletes an context.

deleteDataQualityJobDefinition({required String jobDefinitionName}) → Future<void>

Deletes a data quality monitoring job definition.

deleteDeviceFleet({required String deviceFleetName}) → Future<void>

Deletes a fleet.

deleteDomain({required String domainId, RetentionPolicy? retentionPolicy}) → Future<void>

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.

deleteEdgeDeploymentPlan({required String edgeDeploymentPlanName}) → Future<void>

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.

deleteEdgeDeploymentStage({required String edgeDeploymentPlanName, required String stageName}) → Future<void>

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

deleteEndpoint({required String endpointName}) → Future<void>

Deletes an endpoint. SageMaker frees up all of the resources that were deployed when the endpoint was created.

deleteEndpointConfig({required String endpointConfigName}) → Future<void>

Deletes an endpoint configuration. The DeleteEndpointConfig API deletes only the specified configuration. It does not delete endpoints created using the configuration.

deleteExperiment({required String experimentName}) → Future<DeleteExperimentResponse>

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.

deleteFeatureGroup({required String featureGroupName}) → Future<void>

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.

deleteFlowDefinition({required String flowDefinitionName}) → Future<void>

Deletes the specified flow definition.

deleteHub({required String hubName}) → Future<void>

Delete a hub.

deleteHubContent({required String hubContentName, required HubContentType hubContentType, required String hubContentVersion, required String hubName}) → Future<void>

Delete the contents of a hub.

deleteHubContentReference({required String hubContentName, required HubContentType hubContentType, required String hubName}) → Future<void>

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

deleteHumanTaskUi({required String humanTaskUiName}) → Future<void>

Use this operation to delete a human task user interface (worker task template).

deleteHyperParameterTuningJob({required String hyperParameterTuningJobName}) → Future<void>

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.

deleteImage({required String imageName}) → Future<void>

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

deleteImageVersion({required String imageName, String? alias, int? version}) → Future<void>

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

deleteInferenceComponent({required String inferenceComponentName}) → Future<void>

Deletes an inference component.

deleteInferenceExperiment({required String name}) → Future<DeleteInferenceExperimentResponse>

Deletes an inference experiment.

deleteJob({required JobCategory jobCategory, required String jobName}) → Future<void>

Deletes a job. This operation is idempotent. If the job is currently running, you must stop it before deleting it by calling StopJob.

deleteMlflowApp({required String arn}) → Future<DeleteMlflowAppResponse>

Deletes an MLflow App.

deleteMlflowTrackingServer({required String trackingServerName}) → Future<DeleteMlflowTrackingServerResponse>

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

deleteModel({required String modelName}) → Future<void>

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.

deleteModelBiasJobDefinition({required String jobDefinitionName}) → Future<void>

Deletes an Amazon SageMaker AI model bias job definition.

deleteModelCard({required String modelCardName}) → Future<void>

Deletes an Amazon SageMaker Model Card.

deleteModelExplainabilityJobDefinition({required String jobDefinitionName}) → Future<void>

Deletes an Amazon SageMaker AI model explainability job definition.

deleteModelPackage({required String modelPackageName}) → Future<void>

Deletes a model package.

deleteModelPackageGroup({required String modelPackageGroupName}) → Future<void>

Deletes the specified model group.

deleteModelPackageGroupPolicy({required String modelPackageGroupName}) → Future<void>

Deletes a model group resource policy.

deleteModelQualityJobDefinition({required String jobDefinitionName}) → Future<void>

Deletes the secified model quality monitoring job definition.

deleteMonitoringSchedule({required String monitoringScheduleName}) → Future<void>

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.

deleteNotebookInstance({required String notebookInstanceName}) → Future<void>

Deletes an SageMaker AI notebook instance. Before you can delete a notebook instance, you must call the StopNotebookInstance API.

deleteNotebookInstanceLifecycleConfig({required String notebookInstanceLifecycleConfigName}) → Future<void>

Deletes a notebook instance lifecycle configuration.

deleteOptimizationJob({required String optimizationJobName}) → Future<void>

Deletes an optimization job.

deletePartnerApp({required String arn, String? clientToken}) → Future<DeletePartnerAppResponse>

Deletes a SageMaker Partner AI App.

deletePipeline({required String pipelineName, String? clientRequestToken}) → Future<DeletePipelineResponse>

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.

deleteProcessingJob({required String processingJobName}) → Future<void>

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.

deleteProject({required String projectName}) → Future<void>

Delete the specified project.

deleteSpace({required String domainId, required String spaceName}) → Future<void>

Used to delete a space.

deleteStudioLifecycleConfig({required String studioLifecycleConfigName}) → Future<void>

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.

deleteTags({required String resourceArn, required List<String> tagKeys}) → Future<void>

Deletes the specified tags from an SageMaker resource.

deleteTrainingJob({required String trainingJobName}) → Future<void>

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.

deleteTrial({required String trialName}) → Future<DeleteTrialResponse>

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.

deleteTrialComponent({required String trialComponentName}) → Future<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.

deleteUserProfile({required String domainId, required String userProfileName}) → Future<void>

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

deleteWorkforce({required String workforceName}) → Future<void>

Use this operation to delete a workforce.

deleteWorkteam({required String workteamName}) → Future<DeleteWorkteamResponse>

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

deregisterDevices({required String deviceFleetName, required List<String> deviceNames}) → Future<void>

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

describeAction({required String actionName}) → Future<DescribeActionResponse>

Describes an action.

describeAIBenchmarkJob({required String aIBenchmarkJobName}) → Future<DescribeAIBenchmarkJobResponse>

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

describeAIRecommendationJob({required String aIRecommendationJobName}) → Future<DescribeAIRecommendationJobResponse>

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

describeAIWorkloadConfig({required String aIWorkloadConfigName}) → Future<DescribeAIWorkloadConfigResponse>

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

describeAlgorithm({required String algorithmName}) → Future<DescribeAlgorithmOutput>

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

describeApp({required String appName, required AppType appType, required String domainId, String? spaceName, String? userProfileName}) → Future<DescribeAppResponse>

Describes the app.

describeAppImageConfig({required String appImageConfigName}) → Future<DescribeAppImageConfigResponse>

Describes an AppImageConfig.

describeArtifact({required String artifactArn}) → Future<DescribeArtifactResponse>

Describes an artifact.

describeAutoMLJob({required String autoMLJobName}) → Future<DescribeAutoMLJobResponse>

Returns information about an AutoML job created by calling CreateAutoMLJob.

describeAutoMLJobV2({required String autoMLJobName}) → Future<DescribeAutoMLJobV2Response>

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

describeCluster({required String clusterName}) → Future<DescribeClusterResponse>

Retrieves information of a SageMaker HyperPod cluster.

describeClusterEvent({required String clusterName, required String eventId}) → Future<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.

describeClusterNode({required String clusterName, String? nodeId, String? nodeLogicalId}) → Future<DescribeClusterNodeResponse>

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

describeClusterSchedulerConfig({required String clusterSchedulerConfigId, int? clusterSchedulerConfigVersion}) → Future<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.

describeCodeRepository({required String codeRepositoryName}) → Future<DescribeCodeRepositoryOutput>

Gets details about the specified Git repository.

describeCompilationJob({required String compilationJobName}) → Future<DescribeCompilationJobResponse>

Returns information about a model compilation job.

describeComputeQuota({required String computeQuotaId, int? computeQuotaVersion}) → Future<DescribeComputeQuotaResponse>

Description of the compute allocation definition.

describeContext({required String contextName}) → Future<DescribeContextResponse>

Describes a context.

describeDataQualityJobDefinition({required String jobDefinitionName}) → Future<DescribeDataQualityJobDefinitionResponse>

Gets the details of a data quality monitoring job definition.

describeDevice({required String deviceFleetName, required String deviceName, String? nextToken}) → Future<DescribeDeviceResponse>

Describes the device.

describeDeviceFleet({required String deviceFleetName}) → Future<DescribeDeviceFleetResponse>

A description of the fleet the device belongs to.

describeDomain({required String domainId}) → Future<DescribeDomainResponse>

The description of the domain.

describeEdgeDeploymentPlan({required String edgeDeploymentPlanName, int? maxResults, String? nextToken}) → Future<DescribeEdgeDeploymentPlanResponse>

Describes an edge deployment plan with deployment status per stage.

describeEdgePackagingJob({required String edgePackagingJobName}) → Future<DescribeEdgePackagingJobResponse>

A description of edge packaging jobs.

describeEndpoint({required String endpointName}) → Future<DescribeEndpointOutput>

Returns the description of an endpoint.

describeEndpointConfig({required String endpointConfigName}) → Future<DescribeEndpointConfigOutput>

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

describeExperiment({required String experimentName}) → Future<DescribeExperimentResponse>

Provides a list of an experiment's properties.

describeFeatureGroup({required String featureGroupName, String? nextToken}) → Future<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.

describeFeatureMetadata({required String featureGroupName, required String featureName}) → Future<DescribeFeatureMetadataResponse>

Shows the metadata for a feature within a feature group.

describeFlowDefinition({required String flowDefinitionName}) → Future<DescribeFlowDefinitionResponse>

Returns information about the specified flow definition.

describeHub({required String hubName}) → Future<DescribeHubResponse>

Describes a hub.

describeHubContent({required String hubContentName, required HubContentType hubContentType, required String hubName, String? hubContentVersion}) → Future<DescribeHubContentResponse>

Describe the content of a hub.

describeHumanTaskUi({required String humanTaskUiName}) → Future<DescribeHumanTaskUiResponse>

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

describeHyperParameterTuningJob({required String hyperParameterTuningJobName}) → Future<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.

describeImage({required String imageName}) → Future<DescribeImageResponse>

Describes a SageMaker AI image.

describeImageVersion({required String imageName, String? alias, int? version}) → Future<DescribeImageVersionResponse>

Describes a version of a SageMaker AI image.

describeInferenceComponent({required String inferenceComponentName}) → Future<DescribeInferenceComponentOutput>

Returns information about an inference component.

describeInferenceExperiment({required String name}) → Future<DescribeInferenceExperimentResponse>

Returns details about an inference experiment.

describeInferenceRecommendationsJob({required String jobName}) → Future<DescribeInferenceRecommendationsJobResponse>

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

describeJob({required JobCategory jobCategory, required String jobName}) → Future<DescribeJobResponse>

Returns detailed information about a job, including its current status, secondary status, configuration, and timestamps. Use SecondaryStatus for granular progress tracking and SecondaryStatusTransitions to see the full history of status changes with timestamps.

describeJobSchemaVersion({required JobCategory jobCategory, String? jobConfigSchemaVersion}) → Future<DescribeJobSchemaVersionResponse>

Returns the JSON schema for a specified job category and schema version. Use this schema to validate your JobConfigDocument before calling CreateJob. If you don't specify a schema version, the latest version is returned. The schema defines required fields, allowed values, and constraints for the job configuration.

describeLabelingJob({required String labelingJobName}) → Future<DescribeLabelingJobResponse>

Gets information about a labeling job.

describeLineageGroup({required String lineageGroupName}) → Future<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.

describeMlflowApp({required String arn}) → Future<DescribeMlflowAppResponse>

Returns information about an MLflow App.

describeMlflowTrackingServer({required String trackingServerName}) → Future<DescribeMlflowTrackingServerResponse>

Returns information about an MLflow Tracking Server.

describeModel({required String modelName}) → Future<DescribeModelOutput>

Describes a model that you created using the CreateModel API.

describeModelBiasJobDefinition({required String jobDefinitionName}) → Future<DescribeModelBiasJobDefinitionResponse>

Returns a description of a model bias job definition.

describeModelCard({required String modelCardName, IncludedData? includedData, int? modelCardVersion}) → Future<DescribeModelCardResponse>

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

describeModelCardExportJob({required String modelCardExportJobArn}) → Future<DescribeModelCardExportJobResponse>

Describes an Amazon SageMaker Model Card export job.

describeModelExplainabilityJobDefinition({required String jobDefinitionName}) → Future<DescribeModelExplainabilityJobDefinitionResponse>

Returns a description of a model explainability job definition.

describeModelPackage({required String modelPackageName, IncludedData? includedData}) → Future<DescribeModelPackageOutput>

Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace. To create models in SageMaker, buyers can subscribe to model packages listed on Amazon Web Services Marketplace.

describeModelPackageGroup({required String modelPackageGroupName}) → Future<DescribeModelPackageGroupOutput>

Gets a description for the specified model group.

describeModelQualityJobDefinition({required String jobDefinitionName}) → Future<DescribeModelQualityJobDefinitionResponse>

Returns a description of a model quality job definition.

describeMonitoringSchedule({required String monitoringScheduleName}) → Future<DescribeMonitoringScheduleResponse>

Describes the schedule for a monitoring job.

describeNotebookInstance({required String notebookInstanceName}) → Future<DescribeNotebookInstanceOutput>

Returns information about a notebook instance.

describeNotebookInstanceLifecycleConfig({required String notebookInstanceLifecycleConfigName}) → Future<DescribeNotebookInstanceLifecycleConfigOutput>

Returns a description of a notebook instance lifecycle configuration.

describeOptimizationJob({required String optimizationJobName}) → Future<DescribeOptimizationJobResponse>

Provides the properties of the specified optimization job.

describePartnerApp({required String arn, bool? includeAvailableUpgrade}) → Future<DescribePartnerAppResponse>

Gets information about a SageMaker Partner AI App.

describePipeline({required String pipelineName, int? pipelineVersionId}) → Future<DescribePipelineResponse>

Describes the details of a pipeline.

describePipelineDefinitionForExecution({required String pipelineExecutionArn}) → Future<DescribePipelineDefinitionForExecutionResponse>

Describes the details of an execution's pipeline definition.

describePipelineExecution({required String pipelineExecutionArn}) → Future<DescribePipelineExecutionResponse>

Describes the details of a pipeline execution.

describeProcessingJob({required String processingJobName}) → Future<DescribeProcessingJobResponse>

Returns a description of a processing job.

describeProject({required String projectName}) → Future<DescribeProjectOutput>

Describes the details of a project.

describeReservedCapacity({required String reservedCapacityArn}) → Future<DescribeReservedCapacityResponse>

Retrieves details about a reserved capacity.

describeSpace({required String domainId, required String spaceName}) → Future<DescribeSpaceResponse>

Describes the space.

describeStudioLifecycleConfig({required String studioLifecycleConfigName}) → Future<DescribeStudioLifecycleConfigResponse>

Describes the Amazon SageMaker AI Studio Lifecycle Configuration.

describeSubscribedWorkteam({required String workteamArn}) → Future<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.

describeTrainingJob({required String trainingJobName}) → Future<DescribeTrainingJobResponse>

Returns information about a training job.

describeTrainingPlan({required String trainingPlanName}) → Future<DescribeTrainingPlanResponse>

Retrieves detailed information about a specific training plan.

describeTrainingPlanExtensionHistory({required String trainingPlanArn, int? maxResults, String? nextToken}) → Future<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.

describeTransformJob({required String transformJobName}) → Future<DescribeTransformJobResponse>

Returns information about a transform job.

describeTrial({required String trialName}) → Future<DescribeTrialResponse>

Provides a list of a trial's properties.

describeTrialComponent({required String trialComponentName}) → Future<DescribeTrialComponentResponse>

Provides a list of a trials component's properties.

describeUserProfile({required String domainId, required String userProfileName}) → Future<DescribeUserProfileResponse>

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

describeWorkforce({required String workforceName}) → Future<DescribeWorkforceResponse>

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.

describeWorkteam({required String workteamName}) → Future<DescribeWorkteamResponse>

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).

detachClusterNodeVolume({required String clusterArn, required String nodeId, required String volumeId}) → Future<DetachClusterNodeVolumeResponse>

Detaches your Amazon Elastic Block Store (Amazon EBS) volume from a node in your EKS orchestrated SageMaker HyperPod cluster.

disableSagemakerServicecatalogPortfolio() → Future<void>

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

disassociateTrialComponent({required String trialComponentName, required String trialName}) → Future<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.

enableSagemakerServicecatalogPortfolio() → Future<void>

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

extendTrainingPlan({required String trainingPlanExtensionOfferingId}) → Future<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.

getDeviceFleetReport({required String deviceFleetName}) → Future<GetDeviceFleetReportResponse>

Describes a fleet.

getLineageGroupPolicy({required String lineageGroupName}) → Future<GetLineageGroupPolicyResponse>

The resource policy for the lineage group.

getModelPackageGroupPolicy({required String modelPackageGroupName}) → Future<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..

getSagemakerServicecatalogPortfolioStatus() → Future<GetSagemakerServicecatalogPortfolioStatusOutput>

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

getScalingConfigurationRecommendation({required String inferenceRecommendationsJobName, String? endpointName, String? recommendationId, ScalingPolicyObjective? scalingPolicyObjective, int? targetCpuUtilizationPerCore}) → Future<GetScalingConfigurationRecommendationResponse>

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

getSearchSuggestions({required ResourceType resource, SuggestionQuery? suggestionQuery}) → Future<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.

importHubContent({required String documentSchemaVersion, required String hubContentDocument, required String hubContentName, required HubContentType hubContentType, required String hubName, String? hubContentDescription, String? hubContentDisplayName, String? hubContentMarkdown, List<String>? hubContentSearchKeywords, String? hubContentVersion, HubContentSupportStatus? supportStatus, List<Tag>? tags}) → Future<ImportHubContentResponse>

Import hub content.

listActions({String? actionType, DateTime? createdAfter, DateTime? createdBefore, int? maxResults, String? nextToken, SortActionsBy? sortBy, SortOrder? sortOrder, String? sourceUri}) → Future<ListActionsResponse>

Lists the actions in your account and their properties.

listAIBenchmarkJobs({DateTime? creationTimeAfter, DateTime? creationTimeBefore, int? maxResults, String? nameContains, String? nextToken, ListAIBenchmarkJobsSortBy? sortBy, SortOrder? sortOrder, AIBenchmarkJobStatus? statusEquals}) → Future<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.

listAIRecommendationJobs({DateTime? creationTimeAfter, DateTime? creationTimeBefore, int? maxResults, String? nameContains, String? nextToken, ListAIRecommendationJobsSortBy? sortBy, SortOrder? sortOrder, AIRecommendationJobStatus? statusEquals}) → Future<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.

listAIWorkloadConfigs({DateTime? creationTimeAfter, DateTime? creationTimeBefore, int? maxResults, String? nameContains, String? nextToken, ListAIWorkloadConfigsSortBy? sortBy, SortOrder? sortOrder}) → Future<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.

listAlgorithms({DateTime? creationTimeAfter, DateTime? creationTimeBefore, int? maxResults, String? nameContains, String? nextToken, AlgorithmSortBy? sortBy, SortOrder? sortOrder}) → Future<ListAlgorithmsOutput>

Lists the machine learning algorithms that have been created.

listAliases({required String imageName, String? alias, int? maxResults, String? nextToken, int? version}) → Future<ListAliasesResponse>

Lists the aliases of a specified image or image version.

listAppImageConfigs({DateTime? creationTimeAfter, DateTime? creationTimeBefore, int? maxResults, DateTime? modifiedTimeAfter, DateTime? modifiedTimeBefore, String? nameContains, String? nextToken, AppImageConfigSortKey? sortBy, SortOrder? sortOrder}) → Future<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.

listApps({String? domainIdEquals, int? maxResults, String? nextToken, AppSortKey? sortBy, SortOrder? sortOrder, String? spaceNameEquals, String? userProfileNameEquals}) → Future<ListAppsResponse>

Lists apps.

listArtifacts({String? artifactType, DateTime? createdAfter, DateTime? createdBefore, int? maxResults, String? nextToken, SortArtifactsBy? sortBy, SortOrder? sortOrder, String? sourceUri}) → Future<ListArtifactsResponse>

Lists the artifacts in your account and their properties.

listAssociations({AssociationEdgeType? associationType, DateTime? createdAfter, DateTime? createdBefore, String? destinationArn, String? destinationType, int? maxResults, String? nextToken, SortAssociationsBy? sortBy, SortOrder? sortOrder, String? sourceArn, String? sourceType}) → Future<ListAssociationsResponse>

Lists the associations in your account and their properties.

listAutoMLJobs({DateTime? creationTimeAfter, DateTime? creationTimeBefore, DateTime? lastModifiedTimeAfter, DateTime? lastModifiedTimeBefore, int? maxResults, String? nameContains, String? nextToken, AutoMLSortBy? sortBy, AutoMLSortOrder? sortOrder, AutoMLJobStatus? statusEquals}) → Future<ListAutoMLJobsResponse>

Request a list of jobs.

listCandidatesForAutoMLJob({required String autoMLJobName, String? candidateNameEquals, int? maxResults, String? nextToken, CandidateSortBy? sortBy, AutoMLSortOrder? sortOrder, CandidateStatus? statusEquals}) → Future<ListCandidatesForAutoMLJobResponse>

List the candidates created for the job.

listClusterEvents({required String clusterName, DateTime? eventTimeAfter, DateTime? eventTimeBefore, String? instanceGroupName, int? maxResults, String? nextToken, String? nodeId, ClusterEventResourceType? resourceType, EventSortBy? sortBy, SortOrder? sortOrder}) → Future<ListClusterEventsResponse>

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.

listClusterNodes({required String clusterName, DateTime? creationTimeAfter, DateTime? creationTimeBefore, bool? includeNodeLogicalIds, String? instanceGroupNameContains, int? maxResults, String? nextToken, ClusterSortBy? sortBy, SortOrder? sortOrder}) → Future<ListClusterNodesResponse>

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

listClusters({DateTime? creationTimeAfter, DateTime? creationTimeBefore, int? maxResults, String? nameContains, String? nextToken, ClusterSortBy? sortBy, SortOrder? sortOrder, String? trainingPlanArn}) → Future<ListClustersResponse>

Retrieves the list of SageMaker HyperPod clusters.

listClusterSchedulerConfigs({String? clusterArn, DateTime? createdAfter, DateTime? createdBefore, int? maxResults, String? nameContains, String? nextToken, SortClusterSchedulerConfigBy? sortBy, SortOrder? sortOrder, SchedulerResourceStatus? status}) → Future<ListClusterSchedulerConfigsResponse>

List the cluster policy configurations.

listCodeRepositories({DateTime? creationTimeAfter, DateTime? creationTimeBefore, DateTime? lastModifiedTimeAfter, DateTime? lastModifiedTimeBefore, int? maxResults, String? nameContains, String? nextToken, CodeRepositorySortBy? sortBy, CodeRepositorySortOrder? sortOrder}) → Future<ListCodeRepositoriesOutput>

Gets a list of the Git repositories in your account.

listCompilationJobs({DateTime? creationTimeAfter, DateTime? creationTimeBefore, DateTime? lastModifiedTimeAfter, DateTime? lastModifiedTimeBefore, int? maxResults, String? nameContains, String? nextToken, ListCompilationJobsSortBy? sortBy, SortOrder? sortOrder, CompilationJobStatus? statusEquals}) → Future<ListCompilationJobsResponse>

Lists model compilation jobs that satisfy various filters.

listComputeQuotas({String? clusterArn, DateTime? createdAfter, DateTime? createdBefore, int? maxResults, String? nameContains, String? nextToken, SortQuotaBy? sortBy, SortOrder? sortOrder, SchedulerResourceStatus? status}) → Future<ListComputeQuotasResponse>

List the resource allocation definitions.

listContexts({String? contextType, DateTime? createdAfter, DateTime? createdBefore, int? maxResults, String? nextToken, SortContextsBy? sortBy, SortOrder? sortOrder, String? sourceUri}) → Future<ListContextsResponse>

Lists the contexts in your account and their properties.

listDataQualityJobDefinitions({DateTime? creationTimeAfter, DateTime? creationTimeBefore, String? endpointName, int? maxResults, String? nameContains, String? nextToken, MonitoringJobDefinitionSortKey? sortBy, SortOrder? sortOrder}) → Future<ListDataQualityJobDefinitionsResponse>

Lists the data quality job definitions in your account.

listDeviceFleets({DateTime? creationTimeAfter, DateTime? creationTimeBefore, DateTime? lastModifiedTimeAfter, DateTime? lastModifiedTimeBefore, int? maxResults, String? nameContains, String? nextToken, ListDeviceFleetsSortBy? sortBy, SortOrder? sortOrder}) → Future<ListDeviceFleetsResponse>

Returns a list of devices in the fleet.

listDevices({String? deviceFleetName, DateTime? latestHeartbeatAfter, int? maxResults, String? modelName, String? nextToken}) → Future<ListDevicesResponse>

A list of devices.

listDomains({int? maxResults, String? nextToken}) → Future<ListDomainsResponse>

Lists the domains.

listEdgeDeploymentPlans({DateTime? creationTimeAfter, DateTime? creationTimeBefore, String? deviceFleetNameContains, DateTime? lastModifiedTimeAfter, DateTime? lastModifiedTimeBefore, int? maxResults, String? nameContains, String? nextToken, ListEdgeDeploymentPlansSortBy? sortBy, SortOrder? sortOrder}) → Future<ListEdgeDeploymentPlansResponse>

Lists all edge deployment plans.

listEdgePackagingJobs({DateTime? creationTimeAfter, DateTime? creationTimeBefore, DateTime? lastModifiedTimeAfter, DateTime? lastModifiedTimeBefore, int? maxResults, String? modelNameContains, String? nameContains, String? nextToken, ListEdgePackagingJobsSortBy? sortBy, SortOrder? sortOrder, EdgePackagingJobStatus? statusEquals}) → Future<ListEdgePackagingJobsResponse>

Returns a list of edge packaging jobs.

listEndpointConfigs({DateTime? creationTimeAfter, DateTime? creationTimeBefore, int? maxResults, String? nameContains, String? nextToken, EndpointConfigSortKey? sortBy, OrderKey? sortOrder}) → Future<ListEndpointConfigsOutput>

Lists endpoint configurations.

listEndpoints({DateTime? creationTimeAfter, DateTime? creationTimeBefore, DateTime? lastModifiedTimeAfter, DateTime? lastModifiedTimeBefore, int? maxResults, String? nameContains, String? nextToken, EndpointSortKey? sortBy, OrderKey? sortOrder, EndpointStatus? statusEquals}) → Future<ListEndpointsOutput>

Lists endpoints.

listExperiments({DateTime? createdAfter, DateTime? createdBefore, int? maxResults, String? nextToken, SortExperimentsBy? sortBy, SortOrder? sortOrder}) → Future<ListExperimentsResponse>

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.

listFeatureGroups({DateTime? creationTimeAfter, DateTime? creationTimeBefore, FeatureGroupStatus? featureGroupStatusEquals, int? maxResults, String? nameContains, String? nextToken, OfflineStoreStatusValue? offlineStoreStatusEquals, FeatureGroupSortBy? sortBy, FeatureGroupSortOrder? sortOrder}) → Future<ListFeatureGroupsResponse>

List FeatureGroups based on given filter and order.

listFlowDefinitions({DateTime? creationTimeAfter, DateTime? creationTimeBefore, int? maxResults, String? nextToken, SortOrder? sortOrder}) → Future<ListFlowDefinitionsResponse>

Returns information about the flow definitions in your account.

listHubContents({required HubContentType hubContentType, required String hubName, DateTime? creationTimeAfter, DateTime? creationTimeBefore, int? maxResults, String? maxSchemaVersion, String? nameContains, String? nextToken, HubContentSortBy? sortBy, SortOrder? sortOrder}) → Future<ListHubContentsResponse>

List the contents of a hub.

listHubContentVersions({required String hubContentName, required HubContentType hubContentType, required String hubName, DateTime? creationTimeAfter, DateTime? creationTimeBefore, int? maxResults, String? maxSchemaVersion, String? minVersion, String? nextToken, HubContentSortBy? sortBy, SortOrder? sortOrder}) → Future<ListHubContentVersionsResponse>

List hub content versions.

listHubs({DateTime? creationTimeAfter, DateTime? creationTimeBefore, DateTime? lastModifiedTimeAfter, DateTime? lastModifiedTimeBefore, int? maxResults, String? nameContains, String? nextToken, HubSortBy? sortBy, SortOrder? sortOrder}) → Future<ListHubsResponse>

List all existing hubs.

listHumanTaskUis({DateTime? creationTimeAfter, DateTime? creationTimeBefore, int? maxResults, String? nextToken, SortOrder? sortOrder}) → Future<ListHumanTaskUisResponse>

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

listHyperParameterTuningJobs({DateTime? creationTimeAfter, DateTime? creationTimeBefore, DateTime? lastModifiedTimeAfter, DateTime? lastModifiedTimeBefore, int? maxResults, String? nameContains, String? nextToken, HyperParameterTuningJobSortByOptions? sortBy, SortOrder? sortOrder, HyperParameterTuningJobStatus? statusEquals}) → Future<ListHyperParameterTuningJobsResponse>

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

listImages({DateTime? creationTimeAfter, DateTime? creationTimeBefore, DateTime? lastModifiedTimeAfter, DateTime? lastModifiedTimeBefore, int? maxResults, String? nameContains, String? nextToken, ImageSortBy? sortBy, ImageSortOrder? sortOrder}) → Future<ListImagesResponse>

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.

listImageVersions({required String imageName, DateTime? creationTimeAfter, DateTime? creationTimeBefore, DateTime? lastModifiedTimeAfter, DateTime? lastModifiedTimeBefore, int? maxResults, String? nextToken, ImageVersionSortBy? sortBy, ImageVersionSortOrder? sortOrder}) → Future<ListImageVersionsResponse>

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

listInferenceComponents({DateTime? creationTimeAfter, DateTime? creationTimeBefore, String? endpointNameEquals, DateTime? lastModifiedTimeAfter, DateTime? lastModifiedTimeBefore, int? maxResults, String? nameContains, String? nextToken, InferenceComponentSortKey? sortBy, OrderKey? sortOrder, InferenceComponentStatus? statusEquals, String? variantNameEquals}) → Future<ListInferenceComponentsOutput>

Lists the inference components in your account and their properties.

listInferenceExperiments({DateTime? creationTimeAfter, DateTime? creationTimeBefore, DateTime? lastModifiedTimeAfter, DateTime? lastModifiedTimeBefore, int? maxResults, String? nameContains, String? nextToken, SortInferenceExperimentsBy? sortBy, SortOrder? sortOrder, InferenceExperimentStatus? statusEquals, InferenceExperimentType? type}) → Future<ListInferenceExperimentsResponse>

Returns the list of all inference experiments.

listInferenceRecommendationsJobs({DateTime? creationTimeAfter, DateTime? creationTimeBefore, DateTime? lastModifiedTimeAfter, DateTime? lastModifiedTimeBefore, int? maxResults, String? modelNameEquals, String? modelPackageVersionArnEquals, String? nameContains, String? nextToken, ListInferenceRecommendationsJobsSortBy? sortBy, SortOrder? sortOrder, RecommendationJobStatus? statusEquals}) → Future<ListInferenceRecommendationsJobsResponse>

Lists recommendation jobs that satisfy various filters.

listInferenceRecommendationsJobSteps({required String jobName, int? maxResults, String? nextToken, RecommendationJobStatus? status, RecommendationStepType? stepType}) → Future<ListInferenceRecommendationsJobStepsResponse>

Returns a list of the subtasks for an Inference Recommender job.

listJobs({required JobCategory jobCategory, DateTime? creationTimeAfter, DateTime? creationTimeBefore, DateTime? lastModifiedTimeAfter, DateTime? lastModifiedTimeBefore, int? maxResults, String? nameContains, String? nextToken, SortBy? sortBy, SortOrder? sortOrder, JobStatus? statusEquals}) → Future<ListJobsResponse>

Lists jobs in a specified category. You can filter results by creation time, last modified time, name, and status. Results are sorted by the field you specify in SortBy. Use pagination to retrieve large result sets efficiently.

listJobSchemaVersions({required JobCategory jobCategory, int? maxResults, String? nextToken}) → Future<ListJobSchemaVersionsResponse>

Lists available configuration schema versions for a specified job category. Use the schema versions with DescribeJobSchemaVersion to retrieve the full schema document.

listLabelingJobs({DateTime? creationTimeAfter, DateTime? creationTimeBefore, DateTime? lastModifiedTimeAfter, DateTime? lastModifiedTimeBefore, int? maxResults, String? nameContains, String? nextToken, SortBy? sortBy, SortOrder? sortOrder, LabelingJobStatus? statusEquals}) → Future<ListLabelingJobsResponse>

Gets a list of labeling jobs.

listLabelingJobsForWorkteam({required String workteamArn, DateTime? creationTimeAfter, DateTime? creationTimeBefore, String? jobReferenceCodeContains, int? maxResults, String? nextToken, ListLabelingJobsForWorkteamSortByOptions? sortBy, SortOrder? sortOrder}) → Future<ListLabelingJobsForWorkteamResponse>

Gets a list of labeling jobs assigned to a specified work team.

listLineageGroups({DateTime? createdAfter, DateTime? createdBefore, int? maxResults, String? nextToken, SortLineageGroupsBy? sortBy, SortOrder? sortOrder}) → Future<ListLineageGroupsResponse>

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.

listMlflowApps({AccountDefaultStatus? accountDefaultStatus, DateTime? createdAfter, DateTime? createdBefore, String? defaultForDomainId, int? maxResults, String? mlflowVersion, String? nextToken, SortMlflowAppBy? sortBy, SortOrder? sortOrder, MlflowAppStatus? status}) → Future<ListMlflowAppsResponse>

Lists all MLflow Apps

listMlflowTrackingServers({DateTime? createdAfter, DateTime? createdBefore, int? maxResults, String? mlflowVersion, String? nextToken, SortTrackingServerBy? sortBy, SortOrder? sortOrder, TrackingServerStatus? trackingServerStatus}) → Future<ListMlflowTrackingServersResponse>

Lists all MLflow Tracking Servers.

listModelBiasJobDefinitions({DateTime? creationTimeAfter, DateTime? creationTimeBefore, String? endpointName, int? maxResults, String? nameContains, String? nextToken, MonitoringJobDefinitionSortKey? sortBy, SortOrder? sortOrder}) → Future<ListModelBiasJobDefinitionsResponse>

Lists model bias jobs definitions that satisfy various filters.

listModelCardExportJobs({required String modelCardName, DateTime? creationTimeAfter, DateTime? creationTimeBefore, int? maxResults, String? modelCardExportJobNameContains, int? modelCardVersion, String? nextToken, ModelCardExportJobSortBy? sortBy, ModelCardExportJobSortOrder? sortOrder, ModelCardExportJobStatus? statusEquals}) → Future<ListModelCardExportJobsResponse>

List the export jobs for the Amazon SageMaker Model Card.

listModelCards({DateTime? creationTimeAfter, DateTime? creationTimeBefore, int? maxResults, ModelCardStatus? modelCardStatus, String? nameContains, String? nextToken, ModelCardSortBy? sortBy, ModelCardSortOrder? sortOrder}) → Future<ListModelCardsResponse>

List existing model cards.

listModelCardVersions({required String modelCardName, DateTime? creationTimeAfter, DateTime? creationTimeBefore, int? maxResults, ModelCardStatus? modelCardStatus, String? nextToken, ModelCardVersionSortBy? sortBy, ModelCardSortOrder? sortOrder}) → Future<ListModelCardVersionsResponse>

List existing versions of an Amazon SageMaker Model Card.

listModelExplainabilityJobDefinitions({DateTime? creationTimeAfter, DateTime? creationTimeBefore, String? endpointName, int? maxResults, String? nameContains, String? nextToken, MonitoringJobDefinitionSortKey? sortBy, SortOrder? sortOrder}) → Future<ListModelExplainabilityJobDefinitionsResponse>

Lists model explainability job definitions that satisfy various filters.

listModelMetadata({int? maxResults, String? nextToken, ModelMetadataSearchExpression? searchExpression}) → Future<ListModelMetadataResponse>

Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos.

listModelPackageGroups({DateTime? creationTimeAfter, DateTime? creationTimeBefore, CrossAccountFilterOption? crossAccountFilterOption, int? maxResults, String? nameContains, String? nextToken, ModelPackageGroupSortBy? sortBy, SortOrder? sortOrder}) → Future<ListModelPackageGroupsOutput>

Gets a list of the model groups in your Amazon Web Services account.

listModelPackages({DateTime? creationTimeAfter, DateTime? creationTimeBefore, int? maxResults, ModelApprovalStatus? modelApprovalStatus, String? modelPackageGroupName, ModelPackageType? modelPackageType, String? nameContains, String? nextToken, ModelPackageSortBy? sortBy, SortOrder? sortOrder}) → Future<ListModelPackagesOutput>

Lists the model packages that have been created.

listModelQualityJobDefinitions({DateTime? creationTimeAfter, DateTime? creationTimeBefore, String? endpointName, int? maxResults, String? nameContains, String? nextToken, MonitoringJobDefinitionSortKey? sortBy, SortOrder? sortOrder}) → Future<ListModelQualityJobDefinitionsResponse>

Gets a list of model quality monitoring job definitions in your account.

listModels({DateTime? creationTimeAfter, DateTime? creationTimeBefore, int? maxResults, String? nameContains, String? nextToken, ModelSortKey? sortBy, OrderKey? sortOrder}) → Future<ListModelsOutput>

Lists models created with the CreateModel API.

listMonitoringAlertHistory({DateTime? creationTimeAfter, DateTime? creationTimeBefore, int? maxResults, String? monitoringAlertName, String? monitoringScheduleName, String? nextToken, MonitoringAlertHistorySortKey? sortBy, SortOrder? sortOrder, MonitoringAlertStatus? statusEquals}) → Future<ListMonitoringAlertHistoryResponse>

Gets a list of past alerts in a model monitoring schedule.

listMonitoringAlerts({required String monitoringScheduleName, int? maxResults, String? nextToken}) → Future<ListMonitoringAlertsResponse>

Gets the alerts for a single monitoring schedule.

listMonitoringExecutions({DateTime? creationTimeAfter, DateTime? creationTimeBefore, String? endpointName, DateTime? lastModifiedTimeAfter, DateTime? lastModifiedTimeBefore, int? maxResults, String? monitoringJobDefinitionName, String? monitoringScheduleName, MonitoringType? monitoringTypeEquals, String? nextToken, DateTime? scheduledTimeAfter, DateTime? scheduledTimeBefore, MonitoringExecutionSortKey? sortBy, SortOrder? sortOrder, ExecutionStatus? statusEquals}) → Future<ListMonitoringExecutionsResponse>

Returns list of all monitoring job executions.

listMonitoringSchedules({DateTime? creationTimeAfter, DateTime? creationTimeBefore, String? endpointName, DateTime? lastModifiedTimeAfter, DateTime? lastModifiedTimeBefore, int? maxResults, String? monitoringJobDefinitionName, MonitoringType? monitoringTypeEquals, String? nameContains, String? nextToken, MonitoringScheduleSortKey? sortBy, SortOrder? sortOrder, ScheduleStatus? statusEquals}) → Future<ListMonitoringSchedulesResponse>

Returns list of all monitoring schedules.

listNotebookInstanceLifecycleConfigs({DateTime? creationTimeAfter, DateTime? creationTimeBefore, DateTime? lastModifiedTimeAfter, DateTime? lastModifiedTimeBefore, int? maxResults, String? nameContains, String? nextToken, NotebookInstanceLifecycleConfigSortKey? sortBy, NotebookInstanceLifecycleConfigSortOrder? sortOrder}) → Future<ListNotebookInstanceLifecycleConfigsOutput>

Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.

listNotebookInstances({String? additionalCodeRepositoryEquals, DateTime? creationTimeAfter, DateTime? creationTimeBefore, String? defaultCodeRepositoryContains, DateTime? lastModifiedTimeAfter, DateTime? lastModifiedTimeBefore, int? maxResults, String? nameContains, String? nextToken, String? notebookInstanceLifecycleConfigNameContains, NotebookInstanceSortKey? sortBy, NotebookInstanceSortOrder? sortOrder, NotebookInstanceStatus? statusEquals}) → Future<ListNotebookInstancesOutput>

Returns a list of the SageMaker AI notebook instances in the requester's account in an Amazon Web Services Region.

listOptimizationJobs({DateTime? creationTimeAfter, DateTime? creationTimeBefore, DateTime? lastModifiedTimeAfter, DateTime? lastModifiedTimeBefore, int? maxResults, String? nameContains, String? nextToken, String? optimizationContains, ListOptimizationJobsSortBy? sortBy, SortOrder? sortOrder, OptimizationJobStatus? statusEquals}) → Future<ListOptimizationJobsResponse>

Lists the optimization jobs in your account and their properties.

listPartnerApps({int? maxResults, String? nextToken}) → Future<ListPartnerAppsResponse>

Lists all of the SageMaker Partner AI Apps in an account.

listPipelineExecutions({required String pipelineName, DateTime? createdAfter, DateTime? createdBefore, int? maxResults, String? nextToken, SortPipelineExecutionsBy? sortBy, SortOrder? sortOrder}) → Future<ListPipelineExecutionsResponse>

Gets a list of the pipeline executions.

listPipelineExecutionSteps({int? maxResults, String? nextToken, String? pipelineExecutionArn, SortOrder? sortOrder}) → Future<ListPipelineExecutionStepsResponse>

Gets a list of PipeLineExecutionStep objects.

listPipelineParametersForExecution({required String pipelineExecutionArn, int? maxResults, String? nextToken}) → Future<ListPipelineParametersForExecutionResponse>

Gets a list of parameters for a pipeline execution.

listPipelines({DateTime? createdAfter, DateTime? createdBefore, int? maxResults, String? nextToken, String? pipelineNamePrefix, SortPipelinesBy? sortBy, SortOrder? sortOrder}) → Future<ListPipelinesResponse>

Gets a list of pipelines.

listPipelineVersions({required String pipelineName, DateTime? createdAfter, DateTime? createdBefore, int? maxResults, String? nextToken, SortOrder? sortOrder}) → Future<ListPipelineVersionsResponse>

Gets a list of all versions of the pipeline.

listProcessingJobs({DateTime? creationTimeAfter, DateTime? creationTimeBefore, DateTime? lastModifiedTimeAfter, DateTime? lastModifiedTimeBefore, int? maxResults, String? nameContains, String? nextToken, SortBy? sortBy, SortOrder? sortOrder, ProcessingJobStatus? statusEquals}) → Future<ListProcessingJobsResponse>

Lists processing jobs that satisfy various filters.

listProjects({DateTime? creationTimeAfter, DateTime? creationTimeBefore, int? maxResults, String? nameContains, String? nextToken, ProjectSortBy? sortBy, ProjectSortOrder? sortOrder}) → Future<ListProjectsOutput>

Gets a list of the projects in an Amazon Web Services account.

listResourceCatalogs({DateTime? creationTimeAfter, DateTime? creationTimeBefore, int? maxResults, String? nameContains, String? nextToken, ResourceCatalogSortBy? sortBy, ResourceCatalogSortOrder? sortOrder}) → Future<ListResourceCatalogsResponse>

Lists Amazon SageMaker Catalogs based on given filters and orders. The maximum number of ResourceCatalogs viewable is 1000.

listSpaces({String? domainIdEquals, int? maxResults, String? nextToken, SpaceSortKey? sortBy, SortOrder? sortOrder, String? spaceNameContains}) → Future<ListSpacesResponse>

Lists spaces.

listStageDevices({required String edgeDeploymentPlanName, required String stageName, bool? excludeDevicesDeployedInOtherStage, int? maxResults, String? nextToken}) → Future<ListStageDevicesResponse>

Lists devices allocated to the stage, containing detailed device information and deployment status.

listStudioLifecycleConfigs({StudioLifecycleConfigAppType? appTypeEquals, DateTime? creationTimeAfter, DateTime? creationTimeBefore, int? maxResults, DateTime? modifiedTimeAfter, DateTime? modifiedTimeBefore, String? nameContains, String? nextToken, StudioLifecycleConfigSortKey? sortBy, SortOrder? sortOrder}) → Future<ListStudioLifecycleConfigsResponse>

Lists the Amazon SageMaker AI Studio Lifecycle Configurations in your Amazon Web Services Account.

listSubscribedWorkteams({int? maxResults, String? nameContains, String? nextToken}) → Future<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.

listTags({required String resourceArn, int? maxResults, String? nextToken}) → Future<ListTagsOutput>

Returns the tags for the specified SageMaker resource.

listTrainingJobs({DateTime? creationTimeAfter, DateTime? creationTimeBefore, DateTime? lastModifiedTimeAfter, DateTime? lastModifiedTimeBefore, int? maxResults, String? nameContains, String? nextToken, SortBy? sortBy, SortOrder? sortOrder, TrainingJobStatus? statusEquals, String? trainingPlanArnEquals, WarmPoolResourceStatus? warmPoolStatusEquals}) → Future<ListTrainingJobsResponse>

Lists training jobs.

listTrainingJobsForHyperParameterTuningJob({required String hyperParameterTuningJobName, int? maxResults, String? nextToken, TrainingJobSortByOptions? sortBy, SortOrder? sortOrder, TrainingJobStatus? statusEquals}) → Future<ListTrainingJobsForHyperParameterTuningJobResponse>

Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.

listTrainingPlans({List<TrainingPlanFilter>? filters, int? maxResults, String? nextToken, TrainingPlanSortBy? sortBy, TrainingPlanSortOrder? sortOrder, DateTime? startTimeAfter, DateTime? startTimeBefore}) → Future<ListTrainingPlansResponse>

Retrieves a list of training plans for the current account.

listTransformJobs({DateTime? creationTimeAfter, DateTime? creationTimeBefore, DateTime? lastModifiedTimeAfter, DateTime? lastModifiedTimeBefore, int? maxResults, String? nameContains, String? nextToken, SortBy? sortBy, SortOrder? sortOrder, TransformJobStatus? statusEquals}) → Future<ListTransformJobsResponse>

Lists transform jobs.

listTrialComponents({DateTime? createdAfter, DateTime? createdBefore, String? experimentName, int? maxResults, String? nextToken, SortTrialComponentsBy? sortBy, SortOrder? sortOrder, String? sourceArn, String? trialName}) → Future<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:

listTrials({DateTime? createdAfter, DateTime? createdBefore, String? experimentName, int? maxResults, String? nextToken, SortTrialsBy? sortBy, SortOrder? sortOrder, String? trialComponentName}) → Future<ListTrialsResponse>

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.

listUltraServersByReservedCapacity({required String reservedCapacityArn, int? maxResults, String? nextToken}) → Future<ListUltraServersByReservedCapacityResponse>

Lists all UltraServers that are part of a specified reserved capacity.

listUserProfiles({String? domainIdEquals, int? maxResults, String? nextToken, UserProfileSortKey? sortBy, SortOrder? sortOrder, String? userProfileNameContains}) → Future<ListUserProfilesResponse>

Lists user profiles.

listWorkforces({int? maxResults, String? nameContains, String? nextToken, ListWorkforcesSortByOptions? sortBy, SortOrder? sortOrder}) → Future<ListWorkforcesResponse>

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.

listWorkteams({int? maxResults, String? nameContains, String? nextToken, ListWorkteamsSortByOptions? sortBy, SortOrder? sortOrder}) → Future<ListWorkteamsResponse>

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.

noSuchMethod(Invocation invocation) → dynamic

Invoked when a nonexistent method or property is accessed.

inherited

putModelPackageGroupPolicy({required String modelPackageGroupName, required String resourcePolicy}) → Future<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..

queryLineage({Direction? direction, QueryFilters? filters, bool? includeEdges, int? maxDepth, int? maxResults, String? nextToken, List<String>? startArns}) → Future<QueryLineageResponse>

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.

registerDevices({required String deviceFleetName, required List<Device> devices, List<Tag>? tags}) → Future<void>

Register devices.

renderUiTemplate({required String roleArn, required RenderableTask task, String? humanTaskUiArn, UiTemplate? uiTemplate}) → Future<RenderUiTemplateResponse>

Renders the UI template so that you can preview the worker's experience.

retryPipelineExecution({required String pipelineExecutionArn, String? clientRequestToken, ParallelismConfiguration? parallelismConfiguration}) → Future<RetryPipelineExecutionResponse>

Retry the execution of the pipeline.

search({required ResourceType resource, CrossAccountFilterOption? crossAccountFilterOption, int? maxResults, String? nextToken, SearchExpression? searchExpression, String? sortBy, SearchSortOrder? sortOrder, List<VisibilityConditions>? visibilityConditions}) → Future<SearchResponse>

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.

searchTrainingPlanOfferings({int? durationHours, DateTime? endTimeBefore, int? instanceCount, ReservedCapacityInstanceType? instanceType, DateTime? startTimeAfter, List<SageMakerResourceName>? targetResources, String? trainingPlanArn, int? ultraServerCount, String? ultraServerType}) → Future<SearchTrainingPlanOfferingsResponse>

Searches for available training plan offerings based on specified criteria.

sendPipelineExecutionStepFailure({required String callbackToken, String? clientRequestToken, String? failureReason}) → Future<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).

sendPipelineExecutionStepSuccess({required String callbackToken, String? clientRequestToken, List<OutputParameter>? outputParameters}) → Future<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).

startClusterHealthCheck({required String clusterName, required List<InstanceGroupHealthCheckConfiguration> deepHealthCheckConfigurations}) → Future<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.

startEdgeDeploymentStage({required String edgeDeploymentPlanName, required String stageName}) → Future<void>

Starts a stage in an edge deployment plan.

startInferenceExperiment({required String name}) → Future<StartInferenceExperimentResponse>

Starts an inference experiment.

startMlflowTrackingServer({required String trackingServerName}) → Future<StartMlflowTrackingServerResponse>

Programmatically start an MLflow Tracking Server.

startMonitoringSchedule({required String monitoringScheduleName}) → Future<void>

Starts a previously stopped monitoring schedule.

startNotebookInstance({required String notebookInstanceName}) → Future<void>

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.

startPipelineExecution({required String pipelineName, String? clientRequestToken, String? mlflowExperimentName, ParallelismConfiguration? parallelismConfiguration, String? pipelineExecutionDescription, String? pipelineExecutionDisplayName, List<Parameter>? pipelineParameters, int? pipelineVersionId, SelectiveExecutionConfig? selectiveExecutionConfig}) → Future<StartPipelineExecutionResponse>

Starts a pipeline execution.

startSession({required String resourceIdentifier}) → Future<StartSessionResponse>

Initiates a remote connection session between a local integrated development environments (IDEs) and a remote SageMaker space.

stopAIBenchmarkJob({required String aIBenchmarkJobName}) → Future<StopAIBenchmarkJobResponse>

Stops a running AI benchmark job.

stopAIRecommendationJob({required String aIRecommendationJobName}) → Future<StopAIRecommendationJobResponse>

Stops a running AI recommendation job.

stopAutoMLJob({required String autoMLJobName}) → Future<void>

A method for forcing a running job to shut down.

stopCompilationJob({required String compilationJobName}) → Future<void>

Stops a model compilation job.

stopEdgeDeploymentStage({required String edgeDeploymentPlanName, required String stageName}) → Future<void>

Stops a stage in an edge deployment plan.

stopEdgePackagingJob({required String edgePackagingJobName}) → Future<void>

Request to stop an edge packaging job.

stopHyperParameterTuningJob({required String hyperParameterTuningJobName}) → Future<void>

Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched.

stopInferenceExperiment({required Map<String, ModelVariantAction> modelVariantActions, required String name, List<ModelVariantConfig>? desiredModelVariants, InferenceExperimentStopDesiredState? desiredState, String? reason}) → Future<StopInferenceExperimentResponse>

Stops an inference experiment.

stopInferenceRecommendationsJob({required String jobName}) → Future<void>

Stops an Inference Recommender job.

stopJob({required JobCategory jobCategory, required String jobName}) → Future<void>

Stops a running job. When you call StopJob, Amazon SageMaker sets the job status to Stopping. After the job stops, the status changes to Stopped. Partial results may be available in the output location if the job was in progress. To delete a stopped job, call DeleteJob.

stopLabelingJob({required String labelingJobName}) → Future<void>

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.

stopMlflowTrackingServer({required String trackingServerName}) → Future<StopMlflowTrackingServerResponse>

Programmatically stop an MLflow Tracking Server.

stopMonitoringSchedule({required String monitoringScheduleName}) → Future<void>

Stops a previously started monitoring schedule.

stopNotebookInstance({required String notebookInstanceName}) → Future<void>

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.

stopOptimizationJob({required String optimizationJobName}) → Future<void>

Ends a running inference optimization job.

stopPipelineExecution({required String pipelineExecutionArn, String? clientRequestToken}) → Future<StopPipelineExecutionResponse>

Stops a pipeline execution.

stopProcessingJob({required String processingJobName}) → Future<void>

Stops a processing job.

stopTrainingJob({required String trainingJobName}) → Future<void>

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.

stopTransformJob({required String transformJobName}) → Future<void>

Stops a batch transform job.

toString() → String

A string representation of this object.

inherited

updateAction({required String actionName, String? description, Map<String, String>? properties, List<String>? propertiesToRemove, ActionStatus? status}) → Future<UpdateActionResponse>

Updates an action.

updateAppImageConfig({required String appImageConfigName, CodeEditorAppImageConfig? codeEditorAppImageConfig, JupyterLabAppImageConfig? jupyterLabAppImageConfig, KernelGatewayImageConfig? kernelGatewayImageConfig}) → Future<UpdateAppImageConfigResponse>

Updates the properties of an AppImageConfig.

updateArtifact({required String artifactArn, String? artifactName, Map<String, String>? properties, List<String>? propertiesToRemove}) → Future<UpdateArtifactResponse>

Updates an artifact.

updateCluster({required String clusterName, ClusterAutoScalingConfig? autoScaling, String? clusterRole, List<ClusterInstanceGroupSpecification>? instanceGroups, List<String>? instanceGroupsToDelete, ClusterNodeProvisioningMode? nodeProvisioningMode, ClusterNodeRecovery? nodeRecovery, ClusterOrchestrator? orchestrator, List<ClusterRestrictedInstanceGroupSpecification>? restrictedInstanceGroups, ClusterRestrictedInstanceGroupsConfig? restrictedInstanceGroupsConfig, ClusterTieredStorageConfig? tieredStorageConfig}) → Future<UpdateClusterResponse>

Updates a SageMaker HyperPod cluster.

updateClusterSchedulerConfig({required String clusterSchedulerConfigId, required int targetVersion, String? description, SchedulerConfig? schedulerConfig}) → Future<UpdateClusterSchedulerConfigResponse>

Update the cluster policy configuration.

updateClusterSoftware({required String clusterName, DeploymentConfiguration? deploymentConfig, String? imageId, List<UpdateClusterSoftwareInstanceGroupSpecification>? instanceGroups}) → Future<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.

updateCodeRepository({required String codeRepositoryName, GitConfigForUpdate? gitConfig}) → Future<UpdateCodeRepositoryOutput>

Updates the specified Git repository with the specified values.

updateComputeQuota({required String computeQuotaId, required int targetVersion, ActivationState? activationState, ComputeQuotaConfig? computeQuotaConfig, ComputeQuotaTarget? computeQuotaTarget, String? description}) → Future<UpdateComputeQuotaResponse>

Update the compute allocation definition.

updateContext({required String contextName, String? description, Map<String, String>? properties, List<String>? propertiesToRemove}) → Future<UpdateContextResponse>

Updates a context.

updateDeviceFleet({required String deviceFleetName, required EdgeOutputConfig outputConfig, String? description, bool? enableIotRoleAlias, String? roleArn}) → Future<void>

Updates a fleet of devices.

updateDevices({required String deviceFleetName, required List<Device> devices}) → Future<void>

Updates one or more devices in a fleet.

updateDomain({required String domainId, AppNetworkAccessType? appNetworkAccessType, AppSecurityGroupManagement? appSecurityGroupManagement, DefaultSpaceSettings? defaultSpaceSettings, UserSettings? defaultUserSettings, DomainSettingsForUpdate? domainSettingsForUpdate, HomeEfsFileSystemCreation? homeEfsFileSystemCreation, List<String>? subnetIds, TagPropagation? tagPropagation, String? vpcId}) → Future<UpdateDomainResponse>

Updates the default settings for new user profiles in the domain.

updateEndpoint({required String endpointConfigName, required String endpointName, DeploymentConfig? deploymentConfig, List<VariantProperty>? excludeRetainedVariantProperties, bool? retainAllVariantProperties, bool? retainDeploymentConfig}) → Future<UpdateEndpointOutput>

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.

updateEndpointWeightsAndCapacities({required List<DesiredWeightAndCapacity> desiredWeightsAndCapacities, required String endpointName}) → Future<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.

updateExperiment({required String experimentName, String? description, String? displayName}) → Future<UpdateExperimentResponse>

Adds, updates, or removes the description of an experiment. Updates the display name of an experiment.

updateFeatureGroup({required String featureGroupName, List<FeatureDefinition>? featureAdditions, OnlineStoreConfigUpdate? onlineStoreConfig, ThroughputConfigUpdate? throughputConfig}) → Future<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.

updateFeatureMetadata({required String featureGroupName, required String featureName, String? description, List<FeatureParameter>? parameterAdditions, List<String>? parameterRemovals}) → Future<void>

Updates the description and parameters of the feature group.

updateHub({required String hubName, String? hubDescription, String? hubDisplayName, List<String>? hubSearchKeywords}) → Future<UpdateHubResponse>

Update a hub.

updateHubContent({required String hubContentName, required HubContentType hubContentType, required String hubContentVersion, required String hubName, String? hubContentDescription, String? hubContentDisplayName, String? hubContentMarkdown, List<String>? hubContentSearchKeywords, HubContentSupportStatus? supportStatus}) → Future<UpdateHubContentResponse>

Updates SageMaker hub content (either a Model or Notebook resource).

updateHubContentReference({required String hubContentName, required HubContentType hubContentType, required String hubName, String? minVersion}) → Future<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.

updateImage({required String imageName, List<String>? deleteProperties, String? description, String? displayName, String? roleArn}) → Future<UpdateImageResponse>

Updates the properties of a SageMaker AI image. To change the image's tags, use the AddTags and DeleteTags APIs.

updateImageVersion({required String imageName, String? alias, List<String>? aliasesToAdd, List<String>? aliasesToDelete, bool? horovod, JobType? jobType, String? mLFramework, Processor? processor, String? programmingLang, String? releaseNotes, VendorGuidance? vendorGuidance, int? version}) → Future<UpdateImageVersionResponse>

Updates the properties of a SageMaker AI image version.

updateInferenceComponent({required String inferenceComponentName, InferenceComponentDeploymentConfig? deploymentConfig, InferenceComponentRuntimeConfig? runtimeConfig, InferenceComponentSpecification? specification, List<InferenceComponentSpecification>? specifications}) → Future<UpdateInferenceComponentOutput>

Updates an inference component.

updateInferenceComponentRuntimeConfig({required InferenceComponentRuntimeConfig desiredRuntimeConfig, required String inferenceComponentName}) → Future<UpdateInferenceComponentRuntimeConfigOutput>

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

updateInferenceExperiment({required String name, InferenceExperimentDataStorageConfig? dataStorageConfig, String? description, List<ModelVariantConfig>? modelVariants, InferenceExperimentSchedule? schedule, ShadowModeConfig? shadowModeConfig}) → Future<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.

updateMlflowApp({required String arn, AccountDefaultStatus? accountDefaultStatus, String? artifactStoreUri, List<String>? defaultDomainIdList, ModelRegistrationMode? modelRegistrationMode, String? name, String? weeklyMaintenanceWindowStart}) → Future<UpdateMlflowAppResponse>

Updates an MLflow App.

updateMlflowTrackingServer({required String trackingServerName, String? artifactStoreUri, bool? automaticModelRegistration, String? s3BucketOwnerAccountId, bool? s3BucketOwnerVerification, TrackingServerSize? trackingServerSize, String? weeklyMaintenanceWindowStart}) → Future<UpdateMlflowTrackingServerResponse>

Updates properties of an existing MLflow Tracking Server.

updateModelCard({required String modelCardName, String? content, ModelCardStatus? modelCardStatus}) → Future<UpdateModelCardResponse>

Update an Amazon SageMaker Model Card.

updateModelPackage({required String modelPackageArn, List<AdditionalInferenceSpecificationDefinition>? additionalInferenceSpecificationsToAdd, String? approvalDescription, String? clientToken, Map<String, String>? customerMetadataProperties, List<String>? customerMetadataPropertiesToRemove, InferenceSpecification? inferenceSpecification, ModelApprovalStatus? modelApprovalStatus, ModelPackageModelCard? modelCard, ModelLifeCycle? modelLifeCycle, ModelPackageRegistrationType? modelPackageRegistrationType, String? sourceUri}) → Future<UpdateModelPackageOutput>

Updates a versioned model.

updateMonitoringAlert({required int datapointsToAlert, required int evaluationPeriod, required String monitoringAlertName, required String monitoringScheduleName}) → Future<UpdateMonitoringAlertResponse>

Update the parameters of a model monitor alert.

updateMonitoringSchedule({required MonitoringScheduleConfig monitoringScheduleConfig, required String monitoringScheduleName}) → Future<UpdateMonitoringScheduleResponse>

Updates a previously created schedule.

updateNotebookInstance({required String notebookInstanceName, List<NotebookInstanceAcceleratorType>? acceleratorTypes, List<String>? additionalCodeRepositories, String? defaultCodeRepository, bool? disassociateAcceleratorTypes, bool? disassociateAdditionalCodeRepositories, bool? disassociateDefaultCodeRepository, bool? disassociateLifecycleConfig, InstanceMetadataServiceConfiguration? instanceMetadataServiceConfiguration, InstanceType? instanceType, IPAddressType? ipAddressType, String? lifecycleConfigName, String? platformIdentifier, String? roleArn, RootAccess? rootAccess, int? volumeSizeInGB}) → Future<void>

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.

updateNotebookInstanceLifecycleConfig({required String notebookInstanceLifecycleConfigName, List<NotebookInstanceLifecycleHook>? onCreate, List<NotebookInstanceLifecycleHook>? onStart}) → Future<void>

Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.

updatePartnerApp({required String arn, String? appVersion, PartnerAppConfig? applicationConfig, String? clientToken, bool? enableAutoMinorVersionUpgrade, bool? enableIamSessionBasedIdentity, PartnerAppMaintenanceConfig? maintenanceConfig, List<Tag>? tags, String? tier}) → Future<UpdatePartnerAppResponse>

Updates all of the SageMaker Partner AI Apps in an account.

updatePipeline({required String pipelineName, ParallelismConfiguration? parallelismConfiguration, String? pipelineDefinition, PipelineDefinitionS3Location? pipelineDefinitionS3Location, String? pipelineDescription, String? pipelineDisplayName, String? roleArn}) → Future<UpdatePipelineResponse>

Updates a pipeline.

updatePipelineExecution({required String pipelineExecutionArn, ParallelismConfiguration? parallelismConfiguration, String? pipelineExecutionDescription, String? pipelineExecutionDisplayName}) → Future<UpdatePipelineExecutionResponse>

Updates a pipeline execution.

updatePipelineVersion({required String pipelineArn, required int pipelineVersionId, String? pipelineVersionDescription, String? pipelineVersionDisplayName}) → Future<UpdatePipelineVersionResponse>

Updates a pipeline version.

updateProject({required String projectName, String? projectDescription, ServiceCatalogProvisioningUpdateDetails? serviceCatalogProvisioningUpdateDetails, List<Tag>? tags, List<UpdateTemplateProvider>? templateProvidersToUpdate}) → Future<UpdateProjectOutput>

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.

updateSpace({required String domainId, required String spaceName, String? spaceDisplayName, SpaceSettings? spaceSettings}) → Future<UpdateSpaceResponse>

Updates the settings of a space.

updateTrainingJob({required String trainingJobName, ProfilerConfigForUpdate? profilerConfig, List<ProfilerRuleConfiguration>? profilerRuleConfigurations, RemoteDebugConfigForUpdate? remoteDebugConfig, ResourceConfigForUpdate? resourceConfig}) → Future<UpdateTrainingJobResponse>

Update a model training job to request a new Debugger profiling configuration or to change warm pool retention length.

updateTrial({required String trialName, String? displayName}) → Future<UpdateTrialResponse>

Updates the display name of a trial.

updateTrialComponent({required String trialComponentName, String? displayName, DateTime? endTime, Map<String, TrialComponentArtifact>? inputArtifacts, List<String>? inputArtifactsToRemove, Map<String, TrialComponentArtifact>? outputArtifacts, List<String>? outputArtifactsToRemove, Map<String, TrialComponentParameterValue>? parameters, List<String>? parametersToRemove, DateTime? startTime, TrialComponentStatus? status}) → Future<UpdateTrialComponentResponse>

Updates one or more properties of a trial component.

updateUserProfile({required String domainId, required String userProfileName, UserSettings? userSettings}) → Future<UpdateUserProfileResponse>

Updates a user profile.

updateWorkforce({required String workforceName, WorkforceIpAddressType? ipAddressType, OidcConfig? oidcConfig, SourceIpConfig? sourceIpConfig, WorkforceVpcConfigRequest? workforceVpcConfig}) → Future<UpdateWorkforceResponse>

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.

updateWorkteam({required String workteamName, String? description, List<MemberDefinition>? memberDefinitions, NotificationConfiguration? notificationConfiguration, WorkerAccessConfiguration? workerAccessConfiguration}) → Future<UpdateWorkteamResponse>

Updates an existing work team with new member definitions or description.

Operators

operator ==(Object other) → bool

The equality operator.

inherited