SageMaker class

Provides APIs for creating and managing Amazon SageMaker resources.

Other Resources:

Constructors

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

Properties

hashCode → int
The hash code for this object.
no setterinherited
runtimeType → Type
A representation of the runtime type of the object.
no setterinherited

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 Amazon 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.
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.
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 Amazon SageMaker and list in the AWS Marketplace.
createApp({required String appName, required AppType appType, required String domainId, required String userProfileName, ResourceSpec? resourceSpec, List<Tag>? tags}) → Future<CreateAppResponse>
Creates a running App for the specified UserProfile. Supported Apps are JupyterServer and KernelGateway. This operation is automatically invoked by Amazon SageMaker Studio 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, KernelGatewayImageConfig? kernelGatewayImageConfig, List<Tag>? tags}) → Future<CreateAppImageConfigResponse>
Creates a configuration for running a SageMaker image as a KernelGateway app. The configuration specifies the Amazon Elastic File System (EFS) 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, ProblemType? problemType, List<Tag>? tags}) → Future<CreateAutoMLJobResponse>
Creates an Autopilot job.
createCodeRepository({required String codeRepositoryName, required GitConfig gitConfig, List<Tag>? tags}) → Future<CreateCodeRepositoryOutput>
Creates a Git repository as a resource in your Amazon SageMaker 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 Amazon SageMaker 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 InputConfig inputConfig, required OutputConfig outputConfig, required String roleArn, required StoppingCondition stoppingCondition, List<Tag>? tags}) → Future<CreateCompilationJobResponse>
Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.
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 Model Monitor.
createDeviceFleet({required String deviceFleetName, required EdgeOutputConfig outputConfig, String? description, String? roleArn, List<Tag>? tags}) → Future<void>
Creates a device fleet.
createDomain({required AuthMode authMode, required UserSettings defaultUserSettings, required String domainName, required List<String> subnetIds, required String vpcId, AppNetworkAccessType? appNetworkAccessType, String? homeEfsFileSystemKmsKeyId, String? kmsKeyId, List<Tag>? tags}) → Future<CreateDomainResponse>
Creates a Domain used by Amazon SageMaker Studio. A domain consists of an associated Amazon Elastic File System (EFS) volume, a list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. An AWS account is limited to one domain per region. Users within a domain can share notebook files and other artifacts with each other.
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, List<Tag>? tags}) → Future<CreateEndpointOutput>
Creates an endpoint using the endpoint configuration specified in the request. Amazon 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, DataCaptureConfig? dataCaptureConfig, String? kmsKeyId, List<Tag>? tags}) → Future<CreateEndpointConfigOutput>
Creates an endpoint configuration that Amazon 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 Amazon 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 Amazon 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 an 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.
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}) → 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 HumanLoopConfig humanLoopConfig, required FlowDefinitionOutputConfig outputConfig, required String roleArn, HumanLoopActivationConfig? humanLoopActivationConfig, HumanLoopRequestSource? humanLoopRequestSource, List<Tag>? tags}) → Future<CreateFlowDefinitionResponse>
Creates a flow definition.
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, 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 image. A SageMaker image is a set of image versions. Each image version represents a container image stored in Amazon Container Registry (ECR). For more information, see Bring your own SageMaker image.
createImageVersion({required String baseImage, required String imageName, String? clientToken}) → Future<CreateImageVersionResponse>
Creates a version of the SageMaker image specified by ImageName. The version represents the Amazon Container Registry (ECR) container image specified by BaseImage.
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.
createModel({required String executionRoleArn, required String modelName, List<ContainerDefinition>? containers, bool? enableNetworkIsolation, ContainerDefinition? primaryContainer, List<Tag>? tags, VpcConfig? vpcConfig}) → Future<CreateModelOutput>
Creates a model in Amazon 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.
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({bool? certifyForMarketplace, String? clientToken, InferenceSpecification? inferenceSpecification, MetadataProperties? metadataProperties, ModelApprovalStatus? modelApprovalStatus, ModelMetrics? modelMetrics, String? modelPackageDescription, String? modelPackageGroupName, String? modelPackageName, SourceAlgorithmSpecification? sourceAlgorithmSpecification, List<Tag>? tags, ModelPackageValidationSpecification? validationSpecification}) → Future<CreateModelPackageOutput>
Creates a model package that you can use to create Amazon SageMaker models or list on AWS Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon SageMaker.
createModelPackageGroup({required String modelPackageGroupName, 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 Model Monitor.
createMonitoringSchedule({required MonitoringScheduleConfig monitoringScheduleConfig, required String monitoringScheduleName, List<Tag>? tags}) → Future<CreateMonitoringScheduleResponse>
Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endoint.
createNotebookInstance({required InstanceType instanceType, required String notebookInstanceName, required String roleArn, List<NotebookInstanceAcceleratorType>? acceleratorTypes, List<String>? additionalCodeRepositories, String? defaultCodeRepository, DirectInternetAccess? directInternetAccess, String? kmsKeyId, String? lifecycleConfigName, RootAccess? rootAccess, List<String>? securityGroupIds, String? subnetId, List<Tag>? tags, int? volumeSizeInGB}) → Future<CreateNotebookInstanceOutput>
Creates an Amazon SageMaker 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}) → 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.
createPipeline({required String pipelineDefinition, required String pipelineName, required String roleArn, String? clientRequestToken, 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? sessionExpirationDurationInSeconds}) → 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 Amazon SageMaker Studio, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System (EFS) volume. This operation can only be called when the authentication mode equals IAM.
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 Amazon SageMaker console, when you choose Open next to a notebook instance, Amazon SageMaker 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, required ServiceCatalogProvisioningDetails serviceCatalogProvisioningDetails, String? projectDescription, List<Tag>? tags}) → 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.
createTrainingJob({required AlgorithmSpecification algorithmSpecification, required OutputDataConfig outputDataConfig, required ResourceConfig resourceConfig, required String roleArn, required StoppingCondition stoppingCondition, required String trainingJobName, CheckpointConfig? checkpointConfig, DebugHookConfig? debugHookConfig, List<DebugRuleConfiguration>? debugRuleConfigurations, bool? enableInterContainerTrafficEncryption, bool? enableManagedSpotTraining, bool? enableNetworkIsolation, ExperimentConfig? experimentConfig, Map<String, String>? hyperParameters, List<Channel>? inputDataConfig, ProfilerConfig? profilerConfig, List<ProfilerRuleConfiguration>? profilerRuleConfigurations, List<Tag>? tags, TensorBoardOutputConfig? tensorBoardOutputConfig, VpcConfig? vpcConfig}) → Future<CreateTrainingJobResponse>
Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.
createTransformJob({required String modelName, required TransformInput transformInput, required String transformJobName, required TransformOutput transformOutput, required TransformResources transformResources, BatchStrategy? batchStrategy, 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 Amazon 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 Amazon 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 Amazon SageMaker Studio. If an administrator invites a person by email or imports them from SSO, 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 (EFS) home directory.
createWorkforce({required String workforceName, CognitoConfig? cognitoConfig, OidcConfig? oidcConfig, SourceIpConfig? sourceIpConfig, List<Tag>? tags}) → Future<CreateWorkforceResponse>
Use this operation to create a workforce. This operation will return an error if a workforce already exists in the AWS Region that you specify. You can only create one workforce in each AWS Region per AWS account.
createWorkteam({required String description, required List<MemberDefinition> memberDefinitions, required String workteamName, NotificationConfiguration? notificationConfiguration, List<Tag>? tags, 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.
deleteAlgorithm({required String algorithmName}) → Future<void>
Removes the specified algorithm from your account.
deleteApp({required String appName, required AppType appType, required String domainId, required 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.
deleteCodeRepository({required String codeRepositoryName}) → Future<void>
Deletes the specified Git repository from your account.
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 SSO. Use with caution. All of the members of the domain will lose access to their EFS volume, including data, notebooks, and other artifacts.
deleteEndpoint({required String endpointName}) → Future<void>
Deletes an endpoint. Amazon 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 Amazon 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.
deleteHumanTaskUi({required String humanTaskUiName}) → Future<void>
Use this operation to delete a human task user interface (worker task template).
deleteImage({required String imageName}) → Future<void>
Deletes a SageMaker image and all versions of the image. The container images aren't deleted.
deleteImageVersion({required String imageName, required int version}) → Future<void>
Deletes a version of a SageMaker image. The container image the version represents isn't deleted.
deleteModel({required String modelName}) → Future<void>
Deletes a model. The DeleteModel API deletes only the model entry that was created in Amazon 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 model bias job definition.
deleteModelExplainabilityJobDefinition({required String jobDefinitionName}) → Future<void>
Deletes an Amazon SageMaker 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 Amazon SageMaker 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.
deletePipeline({required String pipelineName, String? clientRequestToken}) → Future<DeletePipelineResponse>
Deletes a pipeline if there are no in-progress executions.
deleteProject({required String projectName}) → Future<void>
Delete the specified project.
deleteTags({required String resourceArn, required List<String> tagKeys}) → Future<void>
Deletes the specified tags from an Amazon SageMaker resource.
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.
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, required 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 Amazon SageMaker job.
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.
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.
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.
describeFlowDefinition({required String flowDefinitionName}) → Future<DescribeFlowDefinitionResponse>
Returns information about the specified flow definition.
describeHumanTaskUi({required String humanTaskUiName}) → Future<DescribeHumanTaskUiResponse>
Returns information about the requested human task user interface (worker task template).
describeHyperParameterTuningJob({required String hyperParameterTuningJobName}) → Future<DescribeHyperParameterTuningJobResponse>
Gets a description of a hyperparameter tuning job.
describeImage({required String imageName}) → Future<DescribeImageResponse>
Describes a SageMaker image.
describeImageVersion({required String imageName, int? version}) → Future<DescribeImageVersionResponse>
Describes a version of a SageMaker image.
describeLabelingJob({required String labelingJobName}) → Future<DescribeLabelingJobResponse>
Gets information about a labeling job.
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.
describeModelExplainabilityJobDefinition({required String jobDefinitionName}) → Future<DescribeModelExplainabilityJobDefinitionResponse>
Returns a description of a model explainability job definition.
describeModelPackage({required String modelPackageName}) → Future<DescribeModelPackageOutput>
Returns a description of the specified model package, which is used to create Amazon SageMaker models or list them on AWS 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.
describePipeline({required String pipelineName}) → 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.
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 AWS Marketplace.
describeTrainingJob({required String trainingJobName}) → Future<DescribeTrainingJobResponse>
Returns information about a training job.
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 create date, the last updated date, membership information, and the work team's Amazon Resource Name (ARN).
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.
getDeviceFleetReport({required String deviceFleetName}) → Future<GetDeviceFleetReportResponse>
Describes a fleet.
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 AWS 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.
getSearchSuggestions({required ResourceType resource, SuggestionQuery? suggestionQuery}) → Future<GetSearchSuggestionsResponse>
An auto-complete API for the search functionality in the Amazon SageMaker console. It returns suggestions of possible matches for the property name to use in Search queries. Provides suggestions for HyperParameters, Tags, and Metrics.
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.
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.
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? 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.
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.
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.
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.
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.
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.
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.
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.
listModelPackageGroups({DateTime? creationTimeAfter, DateTime? creationTimeBefore, int? maxResults, String? nameContains, String? nextToken, ModelPackageGroupSortBy? sortBy, SortOrder? sortOrder}) → Future<ListModelPackageGroupsOutput>
Gets a list of the model groups in your AWS 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.
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 Amazon SageMaker notebook instances in the requester's account in an AWS Region.
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.
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 AWS account.
listSubscribedWorkteams({int? maxResults, String? nameContains, String? nextToken}) → Future<ListSubscribedWorkteamsResponse>
Gets a list of the work teams that you are subscribed to in the AWS 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 Amazon SageMaker resource.
listTrainingJobs({DateTime? creationTimeAfter, DateTime? creationTimeBefore, DateTime? lastModifiedTimeAfter, DateTime? lastModifiedTimeBefore, int? maxResults, String? nameContains, String? nextToken, SortBy? sortBy, SortOrder? sortOrder, TrainingJobStatus? statusEquals}) → 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.
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.
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 AWS Region. Note that you can only have one private workforce per AWS 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 AWS Identity and Access Management User 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.
Finds Amazon 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.
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, Amazon SageMaker 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? pipelineExecutionDescription, String? pipelineExecutionDisplayName, List<Parameter>? pipelineParameters}) → Future<StartPipelineExecutionResponse>
Starts a pipeline execution.
stopAutoMLJob({required String autoMLJobName}) → Future<void>
A method for forcing the termination of a running job.
stopCompilationJob({required String compilationJobName}) → Future<void>
Stops a model compilation job.
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.
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.
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, Amazon SageMaker disconnects the ML storage volume from it. Amazon SageMaker preserves the ML storage volume. Amazon SageMaker stops charging you for the ML compute instance when you call StopNotebookInstance.
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, Amazon 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 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, 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.
updateCodeRepository({required String codeRepositoryName, GitConfigForUpdate? gitConfig}) → Future<UpdateCodeRepositoryOutput>
Updates the specified Git repository with the specified values.
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, 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, UserSettings? defaultUserSettings}) → 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}) → Future<UpdateEndpointOutput>
Deploys the new EndpointConfig specified in the request, switches to using newly created endpoint, and then deletes resources provisioned for the endpoint using the previous EndpointConfig (there is no availability loss).
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, Amazon 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.
updateImage({required String imageName, List<String>? deleteProperties, String? description, String? displayName, String? roleArn}) → Future<UpdateImageResponse>
Updates the properties of a SageMaker image. To change the image's tags, use the AddTags and DeleteTags APIs.
updateModelPackage({required ModelApprovalStatus modelApprovalStatus, required String modelPackageArn, String? approvalDescription}) → Future<UpdateModelPackageOutput>
Updates a versioned model.
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, InstanceType? instanceType, String? lifecycleConfigName, 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.
updatePipeline({required String pipelineName, String? pipelineDefinition, String? pipelineDescription, String? pipelineDisplayName, String? roleArn}) → Future<UpdatePipelineResponse>
Updates a pipeline.
updatePipelineExecution({required String pipelineExecutionArn, String? pipelineExecutionDescription, String? pipelineExecutionDisplayName}) → Future<UpdatePipelineExecutionResponse>
Updates a pipeline execution.
updateTrainingJob({required String trainingJobName, ProfilerConfigForUpdate? profilerConfig, List<ProfilerRuleConfiguration>? profilerRuleConfigurations}) → Future<UpdateTrainingJobResponse>
Update a model training job to request a new Debugger profiling configuration.
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, OidcConfig? oidcConfig, SourceIpConfig? sourceIpConfig}) → 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}) → Future<UpdateWorkteamResponse>
Updates an existing work team with new member definitions or description.

Operators

operator ==(Object other) → bool
The equality operator.
inherited