Class: Aws::SageMaker::Client
- Inherits:
-
Seahorse::Client::Base
- Object
- Seahorse::Client::Base
- Aws::SageMaker::Client
- Includes:
- ClientStubs
- Defined in:
- gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb
Overview
An API client for SageMaker. To construct a client, you need to configure a :region
and :credentials
.
client = Aws::SageMaker::Client.new(
region: region_name,
credentials: credentials,
# ...
)
For details on configuring region and credentials see the developer guide.
See #initialize for a full list of supported configuration options.
Instance Attribute Summary
Attributes inherited from Seahorse::Client::Base
API Operations collapse
-
#add_association(params = {}) ⇒ Types::AddAssociationResponse
Creates an association between the source and the destination.
-
#add_tags(params = {}) ⇒ Types::AddTagsOutput
Adds or overwrites one or more tags for the specified SageMaker resource.
-
#associate_trial_component(params = {}) ⇒ Types::AssociateTrialComponentResponse
Associates a trial component with a trial.
-
#batch_delete_cluster_nodes(params = {}) ⇒ Types::BatchDeleteClusterNodesResponse
Deletes specific nodes within a SageMaker HyperPod cluster.
-
#batch_describe_model_package(params = {}) ⇒ Types::BatchDescribeModelPackageOutput
This action batch describes a list of versioned model packages.
-
#create_action(params = {}) ⇒ Types::CreateActionResponse
Creates an action.
-
#create_algorithm(params = {}) ⇒ Types::CreateAlgorithmOutput
Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.
-
#create_app(params = {}) ⇒ Types::CreateAppResponse
Creates a running app for the specified UserProfile.
-
#create_app_image_config(params = {}) ⇒ Types::CreateAppImageConfigResponse
Creates a configuration for running a SageMaker AI image as a KernelGateway app.
-
#create_artifact(params = {}) ⇒ Types::CreateArtifactResponse
Creates an artifact.
-
#create_auto_ml_job(params = {}) ⇒ Types::CreateAutoMLJobResponse
Creates an Autopilot job also referred to as Autopilot experiment or AutoML job.
-
#create_auto_ml_job_v2(params = {}) ⇒ Types::CreateAutoMLJobV2Response
Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.
-
#create_cluster(params = {}) ⇒ Types::CreateClusterResponse
Creates a SageMaker HyperPod cluster.
-
#create_cluster_scheduler_config(params = {}) ⇒ Types::CreateClusterSchedulerConfigResponse
Create cluster policy configuration.
-
#create_code_repository(params = {}) ⇒ Types::CreateCodeRepositoryOutput
Creates a Git repository as a resource in your SageMaker AI account.
-
#create_compilation_job(params = {}) ⇒ Types::CreateCompilationJobResponse
Starts a model compilation job.
-
#create_compute_quota(params = {}) ⇒ Types::CreateComputeQuotaResponse
Create compute allocation definition.
-
#create_context(params = {}) ⇒ Types::CreateContextResponse
Creates a context.
-
#create_data_quality_job_definition(params = {}) ⇒ Types::CreateDataQualityJobDefinitionResponse
Creates a definition for a job that monitors data quality and drift.
-
#create_device_fleet(params = {}) ⇒ Struct
Creates a device fleet.
-
#create_domain(params = {}) ⇒ Types::CreateDomainResponse
Creates a
Domain
. -
#create_edge_deployment_plan(params = {}) ⇒ Types::CreateEdgeDeploymentPlanResponse
Creates an edge deployment plan, consisting of multiple stages.
-
#create_edge_deployment_stage(params = {}) ⇒ Struct
Creates a new stage in an existing edge deployment plan.
-
#create_edge_packaging_job(params = {}) ⇒ Struct
Starts a SageMaker Edge Manager model packaging job.
-
#create_endpoint(params = {}) ⇒ Types::CreateEndpointOutput
Creates an endpoint using the endpoint configuration specified in the request.
-
#create_endpoint_config(params = {}) ⇒ Types::CreateEndpointConfigOutput
Creates an endpoint configuration that SageMaker hosting services uses to deploy models.
-
#create_experiment(params = {}) ⇒ Types::CreateExperimentResponse
Creates a SageMaker experiment.
-
#create_feature_group(params = {}) ⇒ Types::CreateFeatureGroupResponse
Create a new
FeatureGroup
. -
#create_flow_definition(params = {}) ⇒ Types::CreateFlowDefinitionResponse
Creates a flow definition.
-
#create_hub(params = {}) ⇒ Types::CreateHubResponse
Create a hub.
-
#create_hub_content_reference(params = {}) ⇒ Types::CreateHubContentReferenceResponse
Create a hub content reference in order to add a model in the JumpStart public hub to a private hub.
-
#create_human_task_ui(params = {}) ⇒ Types::CreateHumanTaskUiResponse
Defines the settings you will use for the human review workflow user interface.
-
#create_hyper_parameter_tuning_job(params = {}) ⇒ Types::CreateHyperParameterTuningJobResponse
Starts a hyperparameter tuning job.
-
#create_image(params = {}) ⇒ Types::CreateImageResponse
Creates a custom SageMaker AI image.
-
#create_image_version(params = {}) ⇒ Types::CreateImageVersionResponse
Creates a version of the SageMaker AI image specified by
ImageName
. -
#create_inference_component(params = {}) ⇒ Types::CreateInferenceComponentOutput
Creates an inference component, which is a SageMaker AI hosting object that you can use to deploy a model to an endpoint.
-
#create_inference_experiment(params = {}) ⇒ Types::CreateInferenceExperimentResponse
Creates an inference experiment using the configurations specified in the request.
-
#create_inference_recommendations_job(params = {}) ⇒ Types::CreateInferenceRecommendationsJobResponse
Starts a recommendation job.
-
#create_labeling_job(params = {}) ⇒ Types::CreateLabelingJobResponse
Creates a job that uses workers to label the data objects in your input dataset.
-
#create_mlflow_tracking_server(params = {}) ⇒ Types::CreateMlflowTrackingServerResponse
Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store.
-
#create_model(params = {}) ⇒ Types::CreateModelOutput
Creates a model in SageMaker.
-
#create_model_bias_job_definition(params = {}) ⇒ Types::CreateModelBiasJobDefinitionResponse
Creates the definition for a model bias job.
-
#create_model_card(params = {}) ⇒ Types::CreateModelCardResponse
Creates an Amazon SageMaker Model Card.
-
#create_model_card_export_job(params = {}) ⇒ Types::CreateModelCardExportJobResponse
Creates an Amazon SageMaker Model Card export job.
-
#create_model_explainability_job_definition(params = {}) ⇒ Types::CreateModelExplainabilityJobDefinitionResponse
Creates the definition for a model explainability job.
-
#create_model_package(params = {}) ⇒ Types::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.
-
#create_model_package_group(params = {}) ⇒ Types::CreateModelPackageGroupOutput
Creates a model group.
-
#create_model_quality_job_definition(params = {}) ⇒ Types::CreateModelQualityJobDefinitionResponse
Creates a definition for a job that monitors model quality and drift.
-
#create_monitoring_schedule(params = {}) ⇒ Types::CreateMonitoringScheduleResponse
Creates a schedule that regularly starts Amazon SageMaker AI Processing Jobs to monitor the data captured for an Amazon SageMaker AI Endpoint.
-
#create_notebook_instance(params = {}) ⇒ Types::CreateNotebookInstanceOutput
Creates an SageMaker AI notebook instance.
-
#create_notebook_instance_lifecycle_config(params = {}) ⇒ Types::CreateNotebookInstanceLifecycleConfigOutput
Creates a lifecycle configuration that you can associate with a notebook instance.
-
#create_optimization_job(params = {}) ⇒ Types::CreateOptimizationJobResponse
Creates a job that optimizes a model for inference performance.
-
#create_partner_app(params = {}) ⇒ Types::CreatePartnerAppResponse
Creates an Amazon SageMaker Partner AI App.
-
#create_partner_app_presigned_url(params = {}) ⇒ Types::CreatePartnerAppPresignedUrlResponse
Creates a presigned URL to access an Amazon SageMaker Partner AI App.
-
#create_pipeline(params = {}) ⇒ Types::CreatePipelineResponse
Creates a pipeline using a JSON pipeline definition.
-
#create_presigned_domain_url(params = {}) ⇒ Types::CreatePresignedDomainUrlResponse
Creates a URL for a specified UserProfile in a Domain.
-
#create_presigned_mlflow_tracking_server_url(params = {}) ⇒ Types::CreatePresignedMlflowTrackingServerUrlResponse
Returns a presigned URL that you can use to connect to the MLflow UI attached to your tracking server.
-
#create_presigned_notebook_instance_url(params = {}) ⇒ Types::CreatePresignedNotebookInstanceUrlOutput
Returns a URL that you can use to connect to the Jupyter server from a notebook instance.
-
#create_processing_job(params = {}) ⇒ Types::CreateProcessingJobResponse
Creates a processing job.
-
#create_project(params = {}) ⇒ Types::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.
-
#create_space(params = {}) ⇒ Types::CreateSpaceResponse
Creates a private space or a space used for real time collaboration in a domain.
-
#create_studio_lifecycle_config(params = {}) ⇒ Types::CreateStudioLifecycleConfigResponse
Creates a new Amazon SageMaker AI Studio Lifecycle Configuration.
-
#create_training_job(params = {}) ⇒ Types::CreateTrainingJobResponse
Starts a model training job.
-
#create_training_plan(params = {}) ⇒ Types::CreateTrainingPlanResponse
Creates a new training plan in SageMaker to reserve compute capacity.
-
#create_transform_job(params = {}) ⇒ Types::CreateTransformJobResponse
Starts a transform job.
-
#create_trial(params = {}) ⇒ Types::CreateTrialResponse
Creates an SageMaker trial.
-
#create_trial_component(params = {}) ⇒ Types::CreateTrialComponentResponse
Creates a trial component, which is a stage of a machine learning trial.
-
#create_user_profile(params = {}) ⇒ Types::CreateUserProfileResponse
Creates a user profile.
-
#create_workforce(params = {}) ⇒ Types::CreateWorkforceResponse
Use this operation to create a workforce.
-
#create_workteam(params = {}) ⇒ Types::CreateWorkteamResponse
Creates a new work team for labeling your data.
-
#delete_action(params = {}) ⇒ Types::DeleteActionResponse
Deletes an action.
-
#delete_algorithm(params = {}) ⇒ Struct
Removes the specified algorithm from your account.
-
#delete_app(params = {}) ⇒ Struct
Used to stop and delete an app.
-
#delete_app_image_config(params = {}) ⇒ Struct
Deletes an AppImageConfig.
-
#delete_artifact(params = {}) ⇒ Types::DeleteArtifactResponse
Deletes an artifact.
-
#delete_association(params = {}) ⇒ Types::DeleteAssociationResponse
Deletes an association.
-
#delete_cluster(params = {}) ⇒ Types::DeleteClusterResponse
Delete a SageMaker HyperPod cluster.
-
#delete_cluster_scheduler_config(params = {}) ⇒ Struct
Deletes the cluster policy of the cluster.
-
#delete_code_repository(params = {}) ⇒ Struct
Deletes the specified Git repository from your account.
-
#delete_compilation_job(params = {}) ⇒ Struct
Deletes the specified compilation job.
-
#delete_compute_quota(params = {}) ⇒ Struct
Deletes the compute allocation from the cluster.
-
#delete_context(params = {}) ⇒ Types::DeleteContextResponse
Deletes an context.
-
#delete_data_quality_job_definition(params = {}) ⇒ Struct
Deletes a data quality monitoring job definition.
-
#delete_device_fleet(params = {}) ⇒ Struct
Deletes a fleet.
-
#delete_domain(params = {}) ⇒ Struct
Used to delete a domain.
-
#delete_edge_deployment_plan(params = {}) ⇒ Struct
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.
-
#delete_edge_deployment_stage(params = {}) ⇒ Struct
Delete a stage in an edge deployment plan if (and only if) the stage is inactive.
-
#delete_endpoint(params = {}) ⇒ Struct
Deletes an endpoint.
-
#delete_endpoint_config(params = {}) ⇒ Struct
Deletes an endpoint configuration.
-
#delete_experiment(params = {}) ⇒ Types::DeleteExperimentResponse
Deletes an SageMaker experiment.
-
#delete_feature_group(params = {}) ⇒ Struct
Delete the
FeatureGroup
and any data that was written to theOnlineStore
of theFeatureGroup
. -
#delete_flow_definition(params = {}) ⇒ Struct
Deletes the specified flow definition.
-
#delete_hub(params = {}) ⇒ Struct
Delete a hub.
-
#delete_hub_content(params = {}) ⇒ Struct
Delete the contents of a hub.
-
#delete_hub_content_reference(params = {}) ⇒ Struct
Delete a hub content reference in order to remove a model from a private hub.
-
#delete_human_task_ui(params = {}) ⇒ Struct
Use this operation to delete a human task user interface (worker task template).
-
#delete_hyper_parameter_tuning_job(params = {}) ⇒ Struct
Deletes a hyperparameter tuning job.
-
#delete_image(params = {}) ⇒ Struct
Deletes a SageMaker AI image and all versions of the image.
-
#delete_image_version(params = {}) ⇒ Struct
Deletes a version of a SageMaker AI image.
-
#delete_inference_component(params = {}) ⇒ Struct
Deletes an inference component.
-
#delete_inference_experiment(params = {}) ⇒ Types::DeleteInferenceExperimentResponse
Deletes an inference experiment.
-
#delete_mlflow_tracking_server(params = {}) ⇒ Types::DeleteMlflowTrackingServerResponse
Deletes an MLflow Tracking Server.
-
#delete_model(params = {}) ⇒ Struct
Deletes a model.
-
#delete_model_bias_job_definition(params = {}) ⇒ Struct
Deletes an Amazon SageMaker AI model bias job definition.
-
#delete_model_card(params = {}) ⇒ Struct
Deletes an Amazon SageMaker Model Card.
-
#delete_model_explainability_job_definition(params = {}) ⇒ Struct
Deletes an Amazon SageMaker AI model explainability job definition.
-
#delete_model_package(params = {}) ⇒ Struct
Deletes a model package.
-
#delete_model_package_group(params = {}) ⇒ Struct
Deletes the specified model group.
-
#delete_model_package_group_policy(params = {}) ⇒ Struct
Deletes a model group resource policy.
-
#delete_model_quality_job_definition(params = {}) ⇒ Struct
Deletes the secified model quality monitoring job definition.
-
#delete_monitoring_schedule(params = {}) ⇒ Struct
Deletes a monitoring schedule.
-
#delete_notebook_instance(params = {}) ⇒ Struct
Deletes an SageMaker AI notebook instance.
-
#delete_notebook_instance_lifecycle_config(params = {}) ⇒ Struct
Deletes a notebook instance lifecycle configuration.
-
#delete_optimization_job(params = {}) ⇒ Struct
Deletes an optimization job.
-
#delete_partner_app(params = {}) ⇒ Types::DeletePartnerAppResponse
Deletes a SageMaker Partner AI App.
-
#delete_pipeline(params = {}) ⇒ Types::DeletePipelineResponse
Deletes a pipeline if there are no running instances of the pipeline.
-
#delete_project(params = {}) ⇒ Struct
Delete the specified project.
-
#delete_space(params = {}) ⇒ Struct
Used to delete a space.
-
#delete_studio_lifecycle_config(params = {}) ⇒ Struct
Deletes the Amazon SageMaker AI Studio Lifecycle Configuration.
-
#delete_tags(params = {}) ⇒ Struct
Deletes the specified tags from an SageMaker resource.
-
#delete_trial(params = {}) ⇒ Types::DeleteTrialResponse
Deletes the specified trial.
-
#delete_trial_component(params = {}) ⇒ Types::DeleteTrialComponentResponse
Deletes the specified trial component.
-
#delete_user_profile(params = {}) ⇒ Struct
Deletes a user profile.
-
#delete_workforce(params = {}) ⇒ Struct
Use this operation to delete a workforce.
-
#delete_workteam(params = {}) ⇒ Types::DeleteWorkteamResponse
Deletes an existing work team.
-
#deregister_devices(params = {}) ⇒ Struct
Deregisters the specified devices.
-
#describe_action(params = {}) ⇒ Types::DescribeActionResponse
Describes an action.
-
#describe_algorithm(params = {}) ⇒ Types::DescribeAlgorithmOutput
Returns a description of the specified algorithm that is in your account.
-
#describe_app(params = {}) ⇒ Types::DescribeAppResponse
Describes the app.
-
#describe_app_image_config(params = {}) ⇒ Types::DescribeAppImageConfigResponse
Describes an AppImageConfig.
-
#describe_artifact(params = {}) ⇒ Types::DescribeArtifactResponse
Describes an artifact.
-
#describe_auto_ml_job(params = {}) ⇒ Types::DescribeAutoMLJobResponse
Returns information about an AutoML job created by calling [CreateAutoMLJob][1].
-
#describe_auto_ml_job_v2(params = {}) ⇒ Types::DescribeAutoMLJobV2Response
Returns information about an AutoML job created by calling [CreateAutoMLJobV2][1] or [CreateAutoMLJob][2].
-
#describe_cluster(params = {}) ⇒ Types::DescribeClusterResponse
Retrieves information of a SageMaker HyperPod cluster.
-
#describe_cluster_node(params = {}) ⇒ Types::DescribeClusterNodeResponse
Retrieves information of a node (also called a instance interchangeably) of a SageMaker HyperPod cluster.
-
#describe_cluster_scheduler_config(params = {}) ⇒ Types::DescribeClusterSchedulerConfigResponse
Description of the cluster policy.
-
#describe_code_repository(params = {}) ⇒ Types::DescribeCodeRepositoryOutput
Gets details about the specified Git repository.
-
#describe_compilation_job(params = {}) ⇒ Types::DescribeCompilationJobResponse
Returns information about a model compilation job.
-
#describe_compute_quota(params = {}) ⇒ Types::DescribeComputeQuotaResponse
Description of the compute allocation definition.
-
#describe_context(params = {}) ⇒ Types::DescribeContextResponse
Describes a context.
-
#describe_data_quality_job_definition(params = {}) ⇒ Types::DescribeDataQualityJobDefinitionResponse
Gets the details of a data quality monitoring job definition.
-
#describe_device(params = {}) ⇒ Types::DescribeDeviceResponse
Describes the device.
-
#describe_device_fleet(params = {}) ⇒ Types::DescribeDeviceFleetResponse
A description of the fleet the device belongs to.
-
#describe_domain(params = {}) ⇒ Types::DescribeDomainResponse
The description of the domain.
-
#describe_edge_deployment_plan(params = {}) ⇒ Types::DescribeEdgeDeploymentPlanResponse
Describes an edge deployment plan with deployment status per stage.
-
#describe_edge_packaging_job(params = {}) ⇒ Types::DescribeEdgePackagingJobResponse
A description of edge packaging jobs.
-
#describe_endpoint(params = {}) ⇒ Types::DescribeEndpointOutput
Returns the description of an endpoint.
-
#describe_endpoint_config(params = {}) ⇒ Types::DescribeEndpointConfigOutput
Returns the description of an endpoint configuration created using the
CreateEndpointConfig
API. -
#describe_experiment(params = {}) ⇒ Types::DescribeExperimentResponse
Provides a list of an experiment's properties.
-
#describe_feature_group(params = {}) ⇒ Types::DescribeFeatureGroupResponse
Use this operation to describe a
FeatureGroup
. -
#describe_feature_metadata(params = {}) ⇒ Types::DescribeFeatureMetadataResponse
Shows the metadata for a feature within a feature group.
-
#describe_flow_definition(params = {}) ⇒ Types::DescribeFlowDefinitionResponse
Returns information about the specified flow definition.
-
#describe_hub(params = {}) ⇒ Types::DescribeHubResponse
Describes a hub.
-
#describe_hub_content(params = {}) ⇒ Types::DescribeHubContentResponse
Describe the content of a hub.
-
#describe_human_task_ui(params = {}) ⇒ Types::DescribeHumanTaskUiResponse
Returns information about the requested human task user interface (worker task template).
-
#describe_hyper_parameter_tuning_job(params = {}) ⇒ Types::DescribeHyperParameterTuningJobResponse
Returns a description of a hyperparameter tuning job, depending on the fields selected.
-
#describe_image(params = {}) ⇒ Types::DescribeImageResponse
Describes a SageMaker AI image.
-
#describe_image_version(params = {}) ⇒ Types::DescribeImageVersionResponse
Describes a version of a SageMaker AI image.
-
#describe_inference_component(params = {}) ⇒ Types::DescribeInferenceComponentOutput
Returns information about an inference component.
-
#describe_inference_experiment(params = {}) ⇒ Types::DescribeInferenceExperimentResponse
Returns details about an inference experiment.
-
#describe_inference_recommendations_job(params = {}) ⇒ Types::DescribeInferenceRecommendationsJobResponse
Provides the results of the Inference Recommender job.
-
#describe_labeling_job(params = {}) ⇒ Types::DescribeLabelingJobResponse
Gets information about a labeling job.
-
#describe_lineage_group(params = {}) ⇒ Types::DescribeLineageGroupResponse
Provides a list of properties for the requested lineage group.
-
#describe_mlflow_tracking_server(params = {}) ⇒ Types::DescribeMlflowTrackingServerResponse
Returns information about an MLflow Tracking Server.
-
#describe_model(params = {}) ⇒ Types::DescribeModelOutput
Describes a model that you created using the
CreateModel
API. -
#describe_model_bias_job_definition(params = {}) ⇒ Types::DescribeModelBiasJobDefinitionResponse
Returns a description of a model bias job definition.
-
#describe_model_card(params = {}) ⇒ Types::DescribeModelCardResponse
Describes the content, creation time, and security configuration of an Amazon SageMaker Model Card.
-
#describe_model_card_export_job(params = {}) ⇒ Types::DescribeModelCardExportJobResponse
Describes an Amazon SageMaker Model Card export job.
-
#describe_model_explainability_job_definition(params = {}) ⇒ Types::DescribeModelExplainabilityJobDefinitionResponse
Returns a description of a model explainability job definition.
-
#describe_model_package(params = {}) ⇒ Types::DescribeModelPackageOutput
Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace.
-
#describe_model_package_group(params = {}) ⇒ Types::DescribeModelPackageGroupOutput
Gets a description for the specified model group.
-
#describe_model_quality_job_definition(params = {}) ⇒ Types::DescribeModelQualityJobDefinitionResponse
Returns a description of a model quality job definition.
-
#describe_monitoring_schedule(params = {}) ⇒ Types::DescribeMonitoringScheduleResponse
Describes the schedule for a monitoring job.
-
#describe_notebook_instance(params = {}) ⇒ Types::DescribeNotebookInstanceOutput
Returns information about a notebook instance.
-
#describe_notebook_instance_lifecycle_config(params = {}) ⇒ Types::DescribeNotebookInstanceLifecycleConfigOutput
Returns a description of a notebook instance lifecycle configuration.
-
#describe_optimization_job(params = {}) ⇒ Types::DescribeOptimizationJobResponse
Provides the properties of the specified optimization job.
-
#describe_partner_app(params = {}) ⇒ Types::DescribePartnerAppResponse
Gets information about a SageMaker Partner AI App.
-
#describe_pipeline(params = {}) ⇒ Types::DescribePipelineResponse
Describes the details of a pipeline.
-
#describe_pipeline_definition_for_execution(params = {}) ⇒ Types::DescribePipelineDefinitionForExecutionResponse
Describes the details of an execution's pipeline definition.
-
#describe_pipeline_execution(params = {}) ⇒ Types::DescribePipelineExecutionResponse
Describes the details of a pipeline execution.
-
#describe_processing_job(params = {}) ⇒ Types::DescribeProcessingJobResponse
Returns a description of a processing job.
-
#describe_project(params = {}) ⇒ Types::DescribeProjectOutput
Describes the details of a project.
-
#describe_space(params = {}) ⇒ Types::DescribeSpaceResponse
Describes the space.
-
#describe_studio_lifecycle_config(params = {}) ⇒ Types::DescribeStudioLifecycleConfigResponse
Describes the Amazon SageMaker AI Studio Lifecycle Configuration.
-
#describe_subscribed_workteam(params = {}) ⇒ Types::DescribeSubscribedWorkteamResponse
Gets information about a work team provided by a vendor.
-
#describe_training_job(params = {}) ⇒ Types::DescribeTrainingJobResponse
Returns information about a training job.
-
#describe_training_plan(params = {}) ⇒ Types::DescribeTrainingPlanResponse
Retrieves detailed information about a specific training plan.
-
#describe_transform_job(params = {}) ⇒ Types::DescribeTransformJobResponse
Returns information about a transform job.
-
#describe_trial(params = {}) ⇒ Types::DescribeTrialResponse
Provides a list of a trial's properties.
-
#describe_trial_component(params = {}) ⇒ Types::DescribeTrialComponentResponse
Provides a list of a trials component's properties.
-
#describe_user_profile(params = {}) ⇒ Types::DescribeUserProfileResponse
Describes a user profile.
-
#describe_workforce(params = {}) ⇒ Types::DescribeWorkforceResponse
Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges ([CIDRs][1]).
-
#describe_workteam(params = {}) ⇒ Types::DescribeWorkteamResponse
Gets information about a specific work team.
-
#disable_sagemaker_servicecatalog_portfolio(params = {}) ⇒ Struct
Disables using Service Catalog in SageMaker.
-
#disassociate_trial_component(params = {}) ⇒ Types::DisassociateTrialComponentResponse
Disassociates a trial component from a trial.
-
#enable_sagemaker_servicecatalog_portfolio(params = {}) ⇒ Struct
Enables using Service Catalog in SageMaker.
-
#get_device_fleet_report(params = {}) ⇒ Types::GetDeviceFleetReportResponse
Describes a fleet.
-
#get_lineage_group_policy(params = {}) ⇒ Types::GetLineageGroupPolicyResponse
The resource policy for the lineage group.
-
#get_model_package_group_policy(params = {}) ⇒ Types::GetModelPackageGroupPolicyOutput
Gets a resource policy that manages access for a model group.
-
#get_sagemaker_servicecatalog_portfolio_status(params = {}) ⇒ Types::GetSagemakerServicecatalogPortfolioStatusOutput
Gets the status of Service Catalog in SageMaker.
-
#get_scaling_configuration_recommendation(params = {}) ⇒ Types::GetScalingConfigurationRecommendationResponse
Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job.
-
#get_search_suggestions(params = {}) ⇒ Types::GetSearchSuggestionsResponse
An auto-complete API for the search functionality in the SageMaker console.
-
#import_hub_content(params = {}) ⇒ Types::ImportHubContentResponse
Import hub content.
-
#list_actions(params = {}) ⇒ Types::ListActionsResponse
Lists the actions in your account and their properties.
-
#list_algorithms(params = {}) ⇒ Types::ListAlgorithmsOutput
Lists the machine learning algorithms that have been created.
-
#list_aliases(params = {}) ⇒ Types::ListAliasesResponse
Lists the aliases of a specified image or image version.
-
#list_app_image_configs(params = {}) ⇒ Types::ListAppImageConfigsResponse
Lists the AppImageConfigs in your account and their properties.
-
#list_apps(params = {}) ⇒ Types::ListAppsResponse
Lists apps.
-
#list_artifacts(params = {}) ⇒ Types::ListArtifactsResponse
Lists the artifacts in your account and their properties.
-
#list_associations(params = {}) ⇒ Types::ListAssociationsResponse
Lists the associations in your account and their properties.
-
#list_auto_ml_jobs(params = {}) ⇒ Types::ListAutoMLJobsResponse
Request a list of jobs.
-
#list_candidates_for_auto_ml_job(params = {}) ⇒ Types::ListCandidatesForAutoMLJobResponse
List the candidates created for the job.
-
#list_cluster_nodes(params = {}) ⇒ Types::ListClusterNodesResponse
Retrieves the list of instances (also called nodes interchangeably) in a SageMaker HyperPod cluster.
-
#list_cluster_scheduler_configs(params = {}) ⇒ Types::ListClusterSchedulerConfigsResponse
List the cluster policy configurations.
-
#list_clusters(params = {}) ⇒ Types::ListClustersResponse
Retrieves the list of SageMaker HyperPod clusters.
-
#list_code_repositories(params = {}) ⇒ Types::ListCodeRepositoriesOutput
Gets a list of the Git repositories in your account.
-
#list_compilation_jobs(params = {}) ⇒ Types::ListCompilationJobsResponse
Lists model compilation jobs that satisfy various filters.
-
#list_compute_quotas(params = {}) ⇒ Types::ListComputeQuotasResponse
List the resource allocation definitions.
-
#list_contexts(params = {}) ⇒ Types::ListContextsResponse
Lists the contexts in your account and their properties.
-
#list_data_quality_job_definitions(params = {}) ⇒ Types::ListDataQualityJobDefinitionsResponse
Lists the data quality job definitions in your account.
-
#list_device_fleets(params = {}) ⇒ Types::ListDeviceFleetsResponse
Returns a list of devices in the fleet.
-
#list_devices(params = {}) ⇒ Types::ListDevicesResponse
A list of devices.
-
#list_domains(params = {}) ⇒ Types::ListDomainsResponse
Lists the domains.
-
#list_edge_deployment_plans(params = {}) ⇒ Types::ListEdgeDeploymentPlansResponse
Lists all edge deployment plans.
-
#list_edge_packaging_jobs(params = {}) ⇒ Types::ListEdgePackagingJobsResponse
Returns a list of edge packaging jobs.
-
#list_endpoint_configs(params = {}) ⇒ Types::ListEndpointConfigsOutput
Lists endpoint configurations.
-
#list_endpoints(params = {}) ⇒ Types::ListEndpointsOutput
Lists endpoints.
-
#list_experiments(params = {}) ⇒ Types::ListExperimentsResponse
Lists all the experiments in your account.
-
#list_feature_groups(params = {}) ⇒ Types::ListFeatureGroupsResponse
List
FeatureGroup
s based on given filter and order. -
#list_flow_definitions(params = {}) ⇒ Types::ListFlowDefinitionsResponse
Returns information about the flow definitions in your account.
-
#list_hub_content_versions(params = {}) ⇒ Types::ListHubContentVersionsResponse
List hub content versions.
-
#list_hub_contents(params = {}) ⇒ Types::ListHubContentsResponse
List the contents of a hub.
-
#list_hubs(params = {}) ⇒ Types::ListHubsResponse
List all existing hubs.
-
#list_human_task_uis(params = {}) ⇒ Types::ListHumanTaskUisResponse
Returns information about the human task user interfaces in your account.
-
#list_hyper_parameter_tuning_jobs(params = {}) ⇒ Types::ListHyperParameterTuningJobsResponse
Gets a list of [HyperParameterTuningJobSummary][1] objects that describe the hyperparameter tuning jobs launched in your account.
-
#list_image_versions(params = {}) ⇒ Types::ListImageVersionsResponse
Lists the versions of a specified image and their properties.
-
#list_images(params = {}) ⇒ Types::ListImagesResponse
Lists the images in your account and their properties.
-
#list_inference_components(params = {}) ⇒ Types::ListInferenceComponentsOutput
Lists the inference components in your account and their properties.
-
#list_inference_experiments(params = {}) ⇒ Types::ListInferenceExperimentsResponse
Returns the list of all inference experiments.
-
#list_inference_recommendations_job_steps(params = {}) ⇒ Types::ListInferenceRecommendationsJobStepsResponse
Returns a list of the subtasks for an Inference Recommender job.
-
#list_inference_recommendations_jobs(params = {}) ⇒ Types::ListInferenceRecommendationsJobsResponse
Lists recommendation jobs that satisfy various filters.
-
#list_labeling_jobs(params = {}) ⇒ Types::ListLabelingJobsResponse
Gets a list of labeling jobs.
-
#list_labeling_jobs_for_workteam(params = {}) ⇒ Types::ListLabelingJobsForWorkteamResponse
Gets a list of labeling jobs assigned to a specified work team.
-
#list_lineage_groups(params = {}) ⇒ Types::ListLineageGroupsResponse
A list of lineage groups shared with your Amazon Web Services account.
-
#list_mlflow_tracking_servers(params = {}) ⇒ Types::ListMlflowTrackingServersResponse
Lists all MLflow Tracking Servers.
-
#list_model_bias_job_definitions(params = {}) ⇒ Types::ListModelBiasJobDefinitionsResponse
Lists model bias jobs definitions that satisfy various filters.
-
#list_model_card_export_jobs(params = {}) ⇒ Types::ListModelCardExportJobsResponse
List the export jobs for the Amazon SageMaker Model Card.
-
#list_model_card_versions(params = {}) ⇒ Types::ListModelCardVersionsResponse
List existing versions of an Amazon SageMaker Model Card.
-
#list_model_cards(params = {}) ⇒ Types::ListModelCardsResponse
List existing model cards.
-
#list_model_explainability_job_definitions(params = {}) ⇒ Types::ListModelExplainabilityJobDefinitionsResponse
Lists model explainability job definitions that satisfy various filters.
-
#list_model_metadata(params = {}) ⇒ Types::ListModelMetadataResponse
Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos.
-
#list_model_package_groups(params = {}) ⇒ Types::ListModelPackageGroupsOutput
Gets a list of the model groups in your Amazon Web Services account.
-
#list_model_packages(params = {}) ⇒ Types::ListModelPackagesOutput
Lists the model packages that have been created.
-
#list_model_quality_job_definitions(params = {}) ⇒ Types::ListModelQualityJobDefinitionsResponse
Gets a list of model quality monitoring job definitions in your account.
-
#list_models(params = {}) ⇒ Types::ListModelsOutput
Lists models created with the
CreateModel
API. -
#list_monitoring_alert_history(params = {}) ⇒ Types::ListMonitoringAlertHistoryResponse
Gets a list of past alerts in a model monitoring schedule.
-
#list_monitoring_alerts(params = {}) ⇒ Types::ListMonitoringAlertsResponse
Gets the alerts for a single monitoring schedule.
-
#list_monitoring_executions(params = {}) ⇒ Types::ListMonitoringExecutionsResponse
Returns list of all monitoring job executions.
-
#list_monitoring_schedules(params = {}) ⇒ Types::ListMonitoringSchedulesResponse
Returns list of all monitoring schedules.
-
#list_notebook_instance_lifecycle_configs(params = {}) ⇒ Types::ListNotebookInstanceLifecycleConfigsOutput
Lists notebook instance lifestyle configurations created with the [CreateNotebookInstanceLifecycleConfig][1] API.
-
#list_notebook_instances(params = {}) ⇒ Types::ListNotebookInstancesOutput
Returns a list of the SageMaker AI notebook instances in the requester's account in an Amazon Web Services Region.
-
#list_optimization_jobs(params = {}) ⇒ Types::ListOptimizationJobsResponse
Lists the optimization jobs in your account and their properties.
-
#list_partner_apps(params = {}) ⇒ Types::ListPartnerAppsResponse
Lists all of the SageMaker Partner AI Apps in an account.
-
#list_pipeline_execution_steps(params = {}) ⇒ Types::ListPipelineExecutionStepsResponse
Gets a list of
PipeLineExecutionStep
objects. -
#list_pipeline_executions(params = {}) ⇒ Types::ListPipelineExecutionsResponse
Gets a list of the pipeline executions.
-
#list_pipeline_parameters_for_execution(params = {}) ⇒ Types::ListPipelineParametersForExecutionResponse
Gets a list of parameters for a pipeline execution.
-
#list_pipelines(params = {}) ⇒ Types::ListPipelinesResponse
Gets a list of pipelines.
-
#list_processing_jobs(params = {}) ⇒ Types::ListProcessingJobsResponse
Lists processing jobs that satisfy various filters.
-
#list_projects(params = {}) ⇒ Types::ListProjectsOutput
Gets a list of the projects in an Amazon Web Services account.
-
#list_resource_catalogs(params = {}) ⇒ Types::ListResourceCatalogsResponse
Lists Amazon SageMaker Catalogs based on given filters and orders.
-
#list_spaces(params = {}) ⇒ Types::ListSpacesResponse
Lists spaces.
-
#list_stage_devices(params = {}) ⇒ Types::ListStageDevicesResponse
Lists devices allocated to the stage, containing detailed device information and deployment status.
-
#list_studio_lifecycle_configs(params = {}) ⇒ Types::ListStudioLifecycleConfigsResponse
Lists the Amazon SageMaker AI Studio Lifecycle Configurations in your Amazon Web Services Account.
-
#list_subscribed_workteams(params = {}) ⇒ Types::ListSubscribedWorkteamsResponse
Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace.
-
#list_tags(params = {}) ⇒ Types::ListTagsOutput
Returns the tags for the specified SageMaker resource.
-
#list_training_jobs(params = {}) ⇒ Types::ListTrainingJobsResponse
Lists training jobs.
-
#list_training_jobs_for_hyper_parameter_tuning_job(params = {}) ⇒ Types::ListTrainingJobsForHyperParameterTuningJobResponse
Gets a list of [TrainingJobSummary][1] objects that describe the training jobs that a hyperparameter tuning job launched.
-
#list_training_plans(params = {}) ⇒ Types::ListTrainingPlansResponse
Retrieves a list of training plans for the current account.
-
#list_transform_jobs(params = {}) ⇒ Types::ListTransformJobsResponse
Lists transform jobs.
-
#list_trial_components(params = {}) ⇒ Types::ListTrialComponentsResponse
Lists the trial components in your account.
-
#list_trials(params = {}) ⇒ Types::ListTrialsResponse
Lists the trials in your account.
-
#list_user_profiles(params = {}) ⇒ Types::ListUserProfilesResponse
Lists user profiles.
-
#list_workforces(params = {}) ⇒ Types::ListWorkforcesResponse
Use this operation to list all private and vendor workforces in an Amazon Web Services Region.
-
#list_workteams(params = {}) ⇒ Types::ListWorkteamsResponse
Gets a list of private work teams that you have defined in a region.
-
#put_model_package_group_policy(params = {}) ⇒ Types::PutModelPackageGroupPolicyOutput
Adds a resouce policy to control access to a model group.
-
#query_lineage(params = {}) ⇒ Types::QueryLineageResponse
Use this action to inspect your lineage and discover relationships between entities.
-
#register_devices(params = {}) ⇒ Struct
Register devices.
-
#render_ui_template(params = {}) ⇒ Types::RenderUiTemplateResponse
Renders the UI template so that you can preview the worker's experience.
-
#retry_pipeline_execution(params = {}) ⇒ Types::RetryPipelineExecutionResponse
Retry the execution of the pipeline.
-
#search(params = {}) ⇒ Types::SearchResponse
Finds SageMaker resources that match a search query.
-
#search_training_plan_offerings(params = {}) ⇒ Types::SearchTrainingPlanOfferingsResponse
Searches for available training plan offerings based on specified criteria.
-
#send_pipeline_execution_step_failure(params = {}) ⇒ Types::SendPipelineExecutionStepFailureResponse
Notifies the pipeline that the execution of a callback step failed, along with a message describing why.
-
#send_pipeline_execution_step_success(params = {}) ⇒ Types::SendPipelineExecutionStepSuccessResponse
Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters.
-
#start_edge_deployment_stage(params = {}) ⇒ Struct
Starts a stage in an edge deployment plan.
-
#start_inference_experiment(params = {}) ⇒ Types::StartInferenceExperimentResponse
Starts an inference experiment.
-
#start_mlflow_tracking_server(params = {}) ⇒ Types::StartMlflowTrackingServerResponse
Programmatically start an MLflow Tracking Server.
-
#start_monitoring_schedule(params = {}) ⇒ Struct
Starts a previously stopped monitoring schedule.
-
#start_notebook_instance(params = {}) ⇒ Struct
Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume.
-
#start_pipeline_execution(params = {}) ⇒ Types::StartPipelineExecutionResponse
Starts a pipeline execution.
-
#stop_auto_ml_job(params = {}) ⇒ Struct
A method for forcing a running job to shut down.
-
#stop_compilation_job(params = {}) ⇒ Struct
Stops a model compilation job.
-
#stop_edge_deployment_stage(params = {}) ⇒ Struct
Stops a stage in an edge deployment plan.
-
#stop_edge_packaging_job(params = {}) ⇒ Struct
Request to stop an edge packaging job.
-
#stop_hyper_parameter_tuning_job(params = {}) ⇒ Struct
Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched.
-
#stop_inference_experiment(params = {}) ⇒ Types::StopInferenceExperimentResponse
Stops an inference experiment.
-
#stop_inference_recommendations_job(params = {}) ⇒ Struct
Stops an Inference Recommender job.
-
#stop_labeling_job(params = {}) ⇒ Struct
Stops a running labeling job.
-
#stop_mlflow_tracking_server(params = {}) ⇒ Types::StopMlflowTrackingServerResponse
Programmatically stop an MLflow Tracking Server.
-
#stop_monitoring_schedule(params = {}) ⇒ Struct
Stops a previously started monitoring schedule.
-
#stop_notebook_instance(params = {}) ⇒ Struct
Terminates the ML compute instance.
-
#stop_optimization_job(params = {}) ⇒ Struct
Ends a running inference optimization job.
-
#stop_pipeline_execution(params = {}) ⇒ Types::StopPipelineExecutionResponse
Stops a pipeline execution.
-
#stop_processing_job(params = {}) ⇒ Struct
Stops a processing job.
-
#stop_training_job(params = {}) ⇒ Struct
Stops a training job.
-
#stop_transform_job(params = {}) ⇒ Struct
Stops a batch transform job.
-
#update_action(params = {}) ⇒ Types::UpdateActionResponse
Updates an action.
-
#update_app_image_config(params = {}) ⇒ Types::UpdateAppImageConfigResponse
Updates the properties of an AppImageConfig.
-
#update_artifact(params = {}) ⇒ Types::UpdateArtifactResponse
Updates an artifact.
-
#update_cluster(params = {}) ⇒ Types::UpdateClusterResponse
Updates a SageMaker HyperPod cluster.
-
#update_cluster_scheduler_config(params = {}) ⇒ Types::UpdateClusterSchedulerConfigResponse
Update the cluster policy configuration.
-
#update_cluster_software(params = {}) ⇒ Types::UpdateClusterSoftwareResponse
Updates the platform software of a SageMaker HyperPod cluster for security patching.
-
#update_code_repository(params = {}) ⇒ Types::UpdateCodeRepositoryOutput
Updates the specified Git repository with the specified values.
-
#update_compute_quota(params = {}) ⇒ Types::UpdateComputeQuotaResponse
Update the compute allocation definition.
-
#update_context(params = {}) ⇒ Types::UpdateContextResponse
Updates a context.
-
#update_device_fleet(params = {}) ⇒ Struct
Updates a fleet of devices.
-
#update_devices(params = {}) ⇒ Struct
Updates one or more devices in a fleet.
-
#update_domain(params = {}) ⇒ Types::UpdateDomainResponse
Updates the default settings for new user profiles in the domain.
-
#update_endpoint(params = {}) ⇒ Types::UpdateEndpointOutput
Deploys the
EndpointConfig
specified in the request to a new fleet of instances. -
#update_endpoint_weights_and_capacities(params = {}) ⇒ Types::UpdateEndpointWeightsAndCapacitiesOutput
Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint.
-
#update_experiment(params = {}) ⇒ Types::UpdateExperimentResponse
Adds, updates, or removes the description of an experiment.
-
#update_feature_group(params = {}) ⇒ Types::UpdateFeatureGroupResponse
Updates the feature group by either adding features or updating the online store configuration.
-
#update_feature_metadata(params = {}) ⇒ Struct
Updates the description and parameters of the feature group.
-
#update_hub(params = {}) ⇒ Types::UpdateHubResponse
Update a hub.
-
#update_hub_content(params = {}) ⇒ Types::UpdateHubContentResponse
Updates SageMaker hub content (either a
Model
orNotebook
resource). -
#update_hub_content_reference(params = {}) ⇒ Types::UpdateHubContentReferenceResponse
Updates the contents of a SageMaker hub for a
ModelReference
resource. -
#update_image(params = {}) ⇒ Types::UpdateImageResponse
Updates the properties of a SageMaker AI image.
-
#update_image_version(params = {}) ⇒ Types::UpdateImageVersionResponse
Updates the properties of a SageMaker AI image version.
-
#update_inference_component(params = {}) ⇒ Types::UpdateInferenceComponentOutput
Updates an inference component.
-
#update_inference_component_runtime_config(params = {}) ⇒ Types::UpdateInferenceComponentRuntimeConfigOutput
Runtime settings for a model that is deployed with an inference component.
-
#update_inference_experiment(params = {}) ⇒ Types::UpdateInferenceExperimentResponse
Updates an inference experiment that you created.
-
#update_mlflow_tracking_server(params = {}) ⇒ Types::UpdateMlflowTrackingServerResponse
Updates properties of an existing MLflow Tracking Server.
-
#update_model_card(params = {}) ⇒ Types::UpdateModelCardResponse
Update an Amazon SageMaker Model Card.
-
#update_model_package(params = {}) ⇒ Types::UpdateModelPackageOutput
Updates a versioned model.
-
#update_monitoring_alert(params = {}) ⇒ Types::UpdateMonitoringAlertResponse
Update the parameters of a model monitor alert.
-
#update_monitoring_schedule(params = {}) ⇒ Types::UpdateMonitoringScheduleResponse
Updates a previously created schedule.
-
#update_notebook_instance(params = {}) ⇒ Struct
Updates a notebook instance.
-
#update_notebook_instance_lifecycle_config(params = {}) ⇒ Struct
Updates a notebook instance lifecycle configuration created with the [CreateNotebookInstanceLifecycleConfig][1] API.
-
#update_partner_app(params = {}) ⇒ Types::UpdatePartnerAppResponse
Updates all of the SageMaker Partner AI Apps in an account.
-
#update_pipeline(params = {}) ⇒ Types::UpdatePipelineResponse
Updates a pipeline.
-
#update_pipeline_execution(params = {}) ⇒ Types::UpdatePipelineExecutionResponse
Updates a pipeline execution.
-
#update_project(params = {}) ⇒ Types::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.
-
#update_space(params = {}) ⇒ Types::UpdateSpaceResponse
Updates the settings of a space.
-
#update_training_job(params = {}) ⇒ Types::UpdateTrainingJobResponse
Update a model training job to request a new Debugger profiling configuration or to change warm pool retention length.
-
#update_trial(params = {}) ⇒ Types::UpdateTrialResponse
Updates the display name of a trial.
-
#update_trial_component(params = {}) ⇒ Types::UpdateTrialComponentResponse
Updates one or more properties of a trial component.
-
#update_user_profile(params = {}) ⇒ Types::UpdateUserProfileResponse
Updates a user profile.
-
#update_workforce(params = {}) ⇒ Types::UpdateWorkforceResponse
Use this operation to update your workforce.
-
#update_workteam(params = {}) ⇒ Types::UpdateWorkteamResponse
Updates an existing work team with new member definitions or description.
Instance Method Summary collapse
-
#initialize(options) ⇒ Client
constructor
A new instance of Client.
-
#wait_until(waiter_name, params = {}, options = {}) {|w.waiter| ... } ⇒ Boolean
Polls an API operation until a resource enters a desired state.
Methods included from ClientStubs
#api_requests, #stub_data, #stub_responses
Methods inherited from Seahorse::Client::Base
add_plugin, api, clear_plugins, define, new, #operation_names, plugins, remove_plugin, set_api, set_plugins
Methods included from Seahorse::Client::HandlerBuilder
#handle, #handle_request, #handle_response
Constructor Details
#initialize(options) ⇒ Client
Returns a new instance of Client.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 474 def initialize(*args) super end |
Instance Method Details
#add_association(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 536 def add_association(params = {}, = {}) req = build_request(:add_association, params) req.send_request() end |
#add_tags(params = {}) ⇒ Types::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.
Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see Amazon Web Services Tagging Strategies.
Tags
parameter of
CreateHyperParameterTuningJob
Tags
parameter of CreateDomain or CreateUserProfile.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 619 def (params = {}, = {}) req = build_request(:add_tags, params) req.send_request() end |
#associate_trial_component(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 659 def associate_trial_component(params = {}, = {}) req = build_request(:associate_trial_component, params) req.send_request() end |
#batch_delete_cluster_nodes(params = {}) ⇒ Types::BatchDeleteClusterNodesResponse
Deletes specific nodes within a SageMaker HyperPod cluster.
BatchDeleteClusterNodes
accepts a cluster name and a list of node
IDs.
To safeguard your work, back up your data to Amazon S3 or an FSx for Lustre file system before invoking the API on a worker node group. This will help prevent any potential data loss from the instance root volume. For more information about backup, see Use the backup script provided by SageMaker HyperPod.
If you want to invoke this API on an existing cluster, you'll first need to patch the cluster by running the UpdateClusterSoftware API. For more information about patching a cluster, see Update the SageMaker HyperPod platform software of a cluster.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 730 def batch_delete_cluster_nodes(params = {}, = {}) req = build_request(:batch_delete_cluster_nodes, params) req.send_request() end |
#batch_describe_model_package(params = {}) ⇒ Types::BatchDescribeModelPackageOutput
This action batch describes a list of versioned model packages
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 802 def batch_describe_model_package(params = {}, = {}) req = build_request(:batch_describe_model_package, params) req.send_request() end |
#create_action(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 883 def create_action(params = {}, = {}) req = build_request(:create_action, params) req.send_request() end |
#create_algorithm(params = {}) ⇒ Types::CreateAlgorithmOutput
Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1184 def create_algorithm(params = {}, = {}) req = build_request(:create_algorithm, params) req.send_request() end |
#create_app(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1263 def create_app(params = {}, = {}) req = build_request(:create_app, params) req.send_request() end |
#create_app_image_config(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1362 def create_app_image_config(params = {}, = {}) req = build_request(:create_app_image_config, params) req.send_request() end |
#create_artifact(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1438 def create_artifact(params = {}, = {}) req = build_request(:create_artifact, params) req.send_request() end |
#create_auto_ml_job(params = {}) ⇒ Types::CreateAutoMLJobResponse
Creates an Autopilot job also referred to as Autopilot experiment or AutoML job.
An AutoML job in SageMaker AI is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker AI then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AI AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment.
For more information about AutoML jobs, see https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html in the SageMaker AI developer guide.
CreateAutoMLJobV2
can manage tabular problem types identical to
those of its previous version CreateAutoMLJob
, as well as
time-series forecasting, non-tabular problem types such as image or
text classification, and text generation (LLMs fine-tuning).
Find guidelines about how to migrate a CreateAutoMLJob
to
CreateAutoMLJobV2
in Migrate a CreateAutoMLJob to
CreateAutoMLJobV2.
You can find the best-performing model after you run an AutoML job by calling DescribeAutoMLJobV2 (recommended) or DescribeAutoMLJob.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1637 def create_auto_ml_job(params = {}, = {}) req = build_request(:create_auto_ml_job, params) req.send_request() end |
#create_auto_ml_job_v2(params = {}) ⇒ Types::CreateAutoMLJobV2Response
Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.
An AutoML job in SageMaker AI is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker AI then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AI AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment.
For more information about AutoML jobs, see https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html in the SageMaker AI developer guide.
AutoML jobs V2 support various problem types such as regression, binary, and multiclass classification with tabular data, text and image classification, time-series forecasting, and fine-tuning of large language models (LLMs) for text generation.
CreateAutoMLJobV2
can manage tabular problem types identical to
those of its previous version CreateAutoMLJob
, as well as
time-series forecasting, non-tabular problem types such as image or
text classification, and text generation (LLMs fine-tuning).
Find guidelines about how to migrate a CreateAutoMLJob
to
CreateAutoMLJobV2
in Migrate a CreateAutoMLJob to
CreateAutoMLJobV2.
For the list of available problem types supported by
CreateAutoMLJobV2
, see AutoMLProblemTypeConfig.
You can find the best-performing model after you run an AutoML job V2 by calling DescribeAutoMLJobV2.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1955 def create_auto_ml_job_v2(params = {}, = {}) req = build_request(:create_auto_ml_job_v2, params) req.send_request() end |
#create_cluster(params = {}) ⇒ Types::CreateClusterResponse
Creates a 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2098 def create_cluster(params = {}, = {}) req = build_request(:create_cluster, params) req.send_request() end |
#create_cluster_scheduler_config(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2160 def create_cluster_scheduler_config(params = {}, = {}) req = build_request(:create_cluster_scheduler_config, params) req.send_request() end |
#create_code_repository(params = {}) ⇒ Types::CreateCodeRepositoryOutput
Creates a Git repository as a resource in your SageMaker AI account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your SageMaker AI account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with.
The repository can be hosted either in Amazon Web Services CodeCommit or in any other Git repository.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2228 def create_code_repository(params = {}, = {}) req = build_request(:create_code_repository, params) req.send_request() end |
#create_compilation_job(params = {}) ⇒ Types::CreateCompilationJobResponse
Starts a model compilation job. After the model has been compiled, Amazon SageMaker AI saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.
If you choose to host your model using Amazon SageMaker AI hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with Amazon Web Services IoT Greengrass. In that case, deploy them as an ML resource.
In the request body, you provide the following:
A name for the compilation job
Information about the input model artifacts
The output location for the compiled model and the device (target) that the model runs on
The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker AI assumes to perform the model compilation job.
You can also provide a Tag
to track the model compilation job's
resource use and costs. The response body contains the
CompilationJobArn
for the compiled job.
To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2391 def create_compilation_job(params = {}, = {}) req = build_request(:create_compilation_job, params) req.send_request() end |
#create_compute_quota(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2473 def create_compute_quota(params = {}, = {}) req = build_request(:create_compute_quota, params) req.send_request() end |
#create_context(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2540 def create_context(params = {}, = {}) req = build_request(:create_context, params) req.send_request() end |
#create_data_quality_job_definition(params = {}) ⇒ Types::CreateDataQualityJobDefinitionResponse
Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker AI Model Monitor.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2705 def create_data_quality_job_definition(params = {}, = {}) req = build_request(:create_data_quality_job_definition, params) req.send_request() end |
#create_device_fleet(params = {}) ⇒ Struct
Creates a device fleet.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2764 def create_device_fleet(params = {}, = {}) req = build_request(:create_device_fleet, params) req.send_request() end |
#create_domain(params = {}) ⇒ Types::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.
EFS storage
When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files.
SageMaker AI uses the Amazon Web Services Key Management Service (Amazon Web Services KMS) to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, you can specify a customer managed key. For more information, see Protect Data at Rest Using Encryption.
VPC configuration
All traffic between the domain and the Amazon EFS volume is through
the specified VPC and subnets. For other traffic, you can specify the
AppNetworkAccessType
parameter. AppNetworkAccessType
corresponds
to the network access type that you choose when you onboard to the
domain. The following options are available:
PublicInternetOnly
- Non-EFS traffic goes through a VPC managed by Amazon SageMaker AI, which allows internet access. This is the default value.VpcOnly
- All traffic is through the specified VPC and subnets. Internet access is disabled by default. To allow internet access, you must specify a NAT gateway.When internet access is disabled, you won't be able to run a Amazon SageMaker AI Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker AI API and runtime or a NAT gateway and your security groups allow outbound connections.
NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a Amazon SageMaker AI Studio app successfully.
For more information, see Connect Amazon SageMaker AI Studio Notebooks to Resources in a VPC.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3240 def create_domain(params = {}, = {}) req = build_request(:create_domain, params) req.send_request() end |
#create_edge_deployment_plan(params = {}) ⇒ Types::CreateEdgeDeploymentPlanResponse
Creates an edge deployment plan, consisting of multiple stages. Each stage may have a different deployment configuration and devices.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3309 def create_edge_deployment_plan(params = {}, = {}) req = build_request(:create_edge_deployment_plan, params) req.send_request() end |
#create_edge_deployment_stage(params = {}) ⇒ Struct
Creates a new stage in an existing edge deployment plan.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3348 def create_edge_deployment_stage(params = {}, = {}) req = build_request(:create_edge_deployment_stage, params) req.send_request() end |
#create_edge_packaging_job(params = {}) ⇒ Struct
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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3415 def create_edge_packaging_job(params = {}, = {}) req = build_request(:create_edge_packaging_job, params) req.send_request() end |
#create_endpoint(params = {}) ⇒ Types::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.
Use this API to deploy models using SageMaker hosting services.
EndpointConfig
that is in use by an endpoint
that is live or while the UpdateEndpoint
or CreateEndpoint
operations are being performed on the endpoint. To update an endpoint,
you must create a new EndpointConfig
.
The endpoint name must be unique within an Amazon Web Services Region in your Amazon Web Services account.
When it receives the request, SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.
Eventually Consistent Reads
,
the response might not reflect the results of a recently completed
write operation. The response might include some stale data. If the
dependent entities are not yet in DynamoDB, this causes a validation
error. If you repeat your read request after a short time, the
response should return the latest data. So retry logic is recommended
to handle these possible issues. We also recommend that customers call
DescribeEndpointConfig before calling CreateEndpoint to
minimize the potential impact of a DynamoDB eventually consistent
read.
When SageMaker receives the request, it sets the endpoint status to
Creating
. After it creates the endpoint, it sets the status to
InService
. SageMaker can then process incoming requests for
inferences. To check the status of an endpoint, use the
DescribeEndpoint API.
If any of the models hosted at this endpoint get model data from an Amazon S3 location, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.
Option 1: For a full SageMaker access, search and attach the
AmazonSageMakerFullAccess
policy.Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the JSON file of the IAM role:
"Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"]
"Resource": [
"arn:aws:sagemaker:region:account-id:endpoint/endpointName"
"arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName"
]
For more information, see SageMaker API Permissions: Actions, Permissions, and Resources Reference.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3606 def create_endpoint(params = {}, = {}) req = build_request(:create_endpoint, params) req.send_request() end |
#create_endpoint_config(params = {}) ⇒ Types::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.
If you are hosting multiple models, you also assign a VariantWeight
to specify how much traffic you want to allocate to each model. For
example, suppose that you want to host two models, A and B, and you
assign traffic weight 2 for model A and 1 for model B. SageMaker
distributes two-thirds of the traffic to Model A, and one-third to
model B.
Eventually Consistent Reads
,
the response might not reflect the results of a recently completed
write operation. The response might include some stale data. If the
dependent entities are not yet in DynamoDB, this causes a validation
error. If you repeat your read request after a short time, the
response should return the latest data. So retry logic is recommended
to handle these possible issues. We also recommend that customers call
DescribeEndpointConfig before calling CreateEndpoint to
minimize the potential impact of a DynamoDB eventually consistent
read.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3932 def create_endpoint_config(params = {}, = {}) req = build_request(:create_endpoint_config, params) req.send_request() end |
#create_experiment(params = {}) ⇒ Types::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.
When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to experiments, trials, trial components and then use the Search API to search for the tags.
To add a description to an experiment, specify the optional
Description
parameter. To add a description later, or to change the
description, call the UpdateExperiment API.
To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To create a trial call the CreateTrial API.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4025 def create_experiment(params = {}, = {}) req = build_request(:create_experiment, params) req.send_request() end |
#create_feature_group(params = {}) ⇒ Types::CreateFeatureGroupResponse
Create a new FeatureGroup
. A FeatureGroup
is a group of Features
defined in the FeatureStore
to describe a Record
.
The FeatureGroup
defines the schema and features contained in the
FeatureGroup
. A FeatureGroup
definition is composed of a list of
Features
, a RecordIdentifierFeatureName
, an EventTimeFeatureName
and configurations for its OnlineStore
and OfflineStore
. Check
Amazon Web Services service quotas to see the FeatureGroup
s
quota for your Amazon Web Services account.
Note that it can take approximately 10-15 minutes to provision an
OnlineStore
FeatureGroup
with the InMemory
StorageType
.
You must include at least one of OnlineStoreConfig
and
OfflineStoreConfig
to create a FeatureGroup
.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4250 def create_feature_group(params = {}, = {}) req = build_request(:create_feature_group, params) req.send_request() end |
#create_flow_definition(params = {}) ⇒ Types::CreateFlowDefinitionResponse
Creates a flow definition.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4341 def create_flow_definition(params = {}, = {}) req = build_request(:create_flow_definition, params) req.send_request() end |
#create_hub(params = {}) ⇒ Types::CreateHubResponse
Create a hub.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4396 def create_hub(params = {}, = {}) req = build_request(:create_hub, params) req.send_request() end |
#create_hub_content_reference(params = {}) ⇒ Types::CreateHubContentReferenceResponse
Create a hub content reference in order to add a model in the JumpStart public hub to a private hub.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4448 def create_hub_content_reference(params = {}, = {}) req = build_request(:create_hub_content_reference, params) req.send_request() end |
#create_human_task_ui(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4495 def create_human_task_ui(params = {}, = {}) req = build_request(:create_human_task_ui, params) req.send_request() end |
#create_hyper_parameter_tuning_job(params = {}) ⇒ Types::CreateHyperParameterTuningJobResponse
Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.
A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and trial components for each training job that it runs. You can view these entities in Amazon SageMaker Studio. For more information, see View Experiments, Trials, and Trial Components.
Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5006 def create_hyper_parameter_tuning_job(params = {}, = {}) req = build_request(:create_hyper_parameter_tuning_job, params) req.send_request() end |
#create_image(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5064 def create_image(params = {}, = {}) req = build_request(:create_image, params) req.send_request() end |
#create_image_version(params = {}) ⇒ Types::CreateImageVersionResponse
Creates a version of the SageMaker AI image specified by ImageName
.
The version represents the Amazon ECR container image specified by
BaseImage
.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5169 def create_image_version(params = {}, = {}) req = build_request(:create_image_version, params) req.send_request() end |
#create_inference_component(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5264 def create_inference_component(params = {}, = {}) req = build_request(:create_inference_component, params) req.send_request() end |
#create_inference_experiment(params = {}) ⇒ Types::CreateInferenceExperimentResponse
Creates an inference experiment using the configurations specified in the request.
Use this API to setup and schedule an experiment to compare model variants on a Amazon SageMaker inference endpoint. For more information about inference experiments, see Shadow tests.
Amazon SageMaker begins your experiment at the scheduled time and routes traffic to your endpoint's model variants based on your specified configuration.
While the experiment is in progress or after it has concluded, you can view metrics that compare your model variants. For more information, see View, monitor, and edit shadow tests.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5463 def create_inference_experiment(params = {}, = {}) req = build_request(:create_inference_experiment, params) req.send_request() end |
#create_inference_recommendations_job(params = {}) ⇒ Types::CreateInferenceRecommendationsJobResponse
Starts a recommendation job. You can create either an instance recommendation or load test job.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5626 def create_inference_recommendations_job(params = {}, = {}) req = build_request(:create_inference_recommendations_job, params) req.send_request() end |
#create_labeling_job(params = {}) ⇒ Types::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.
You can select your workforce from one of three providers:
A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required.
One or more vendors that you select from the Amazon Web Services Marketplace. Vendors provide expertise in specific areas.
The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information.
You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling.
The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data.
The output can be used as the manifest file for another labeling job or as training data for your machine learning models.
You can use this operation to create a static labeling job or a
streaming labeling job. A static labeling job stops if all data
objects in the input manifest file identified in ManifestS3Uri
have
been labeled. A streaming labeling job runs perpetually until it is
manually stopped, or remains idle for 10 days. You can send new data
objects to an active (InProgress
) streaming labeling job in real
time. To learn how to create a static labeling job, see Create a
Labeling Job (API) in the Amazon SageMaker Developer Guide. To
learn how to create a streaming labeling job, see Create a Streaming
Labeling Job.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5935 def create_labeling_job(params = {}, = {}) req = build_request(:create_labeling_job, params) req.send_request() end |
#create_mlflow_tracking_server(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6032 def create_mlflow_tracking_server(params = {}, = {}) req = build_request(:create_mlflow_tracking_server, params) req.send_request() end |
#create_model(params = {}) ⇒ Types::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.
Use this API to create a model if you want to use SageMaker hosting services or run a batch transform job.
To host your model, you create an endpoint configuration with the
CreateEndpointConfig
API, and then create an endpoint with the
CreateEndpoint
API. SageMaker then deploys all of the containers
that you defined for the model in the hosting environment.
To run a batch transform using your model, you start a job with the
CreateTransformJob
API. SageMaker uses your model and your dataset
to get inferences which are then saved to a specified S3 location.
In the request, you also provide an IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other Amazon Web Services resources, you grant necessary permissions via this role.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6266 def create_model(params = {}, = {}) req = build_request(:create_model, params) req.send_request() end |
#create_model_bias_job_definition(params = {}) ⇒ Types::CreateModelBiasJobDefinitionResponse
Creates the definition for a model bias job.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6423 def create_model_bias_job_definition(params = {}, = {}) req = build_request(:create_model_bias_job_definition, params) req.send_request() end |
#create_model_card(params = {}) ⇒ Types::CreateModelCardResponse
Creates an Amazon SageMaker Model Card.
For information about how to use model cards, see Amazon SageMaker Model Card.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6499 def create_model_card(params = {}, = {}) req = build_request(:create_model_card, params) req.send_request() end |
#create_model_card_export_job(params = {}) ⇒ Types::CreateModelCardExportJobResponse
Creates an Amazon SageMaker Model Card export job.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6543 def create_model_card_export_job(params = {}, = {}) req = build_request(:create_model_card_export_job, params) req.send_request() end |
#create_model_explainability_job_definition(params = {}) ⇒ Types::CreateModelExplainabilityJobDefinitionResponse
Creates the definition for a model explainability job.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6698 def create_model_explainability_job_definition(params = {}, = {}) req = build_request(:create_model_explainability_job_definition, params) req.send_request() end |
#create_model_package(params = {}) ⇒ Types::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.
To create a model package by specifying a Docker container that
contains your inference code and the Amazon S3 location of your model
artifacts, provide values for InferenceSpecification
. To create a
model from an algorithm resource that you created or subscribed to in
Amazon Web Services Marketplace, provide a value for
SourceAlgorithmSpecification
.
Versioned - a model that is part of a model group in the model registry.
Unversioned - a model package that is not part of a model group.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 7202 def create_model_package(params = {}, = {}) req = build_request(:create_model_package, params) req.send_request() end |
#create_model_package_group(params = {}) ⇒ Types::CreateModelPackageGroupOutput
Creates a model group. A model group contains a group of model versions.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 7250 def create_model_package_group(params = {}, = {}) req = build_request(:create_model_package_group, params) req.send_request() end |
#create_model_quality_job_definition(params = {}) ⇒ Types::CreateModelQualityJobDefinitionResponse
Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker AI Model Monitor.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 7416 def create_model_quality_job_definition(params = {}, = {}) req = build_request(:create_model_quality_job_definition, params) req.send_request() end |
#create_monitoring_schedule(params = {}) ⇒ Types::CreateMonitoringScheduleResponse
Creates a schedule that regularly starts Amazon SageMaker AI Processing Jobs to monitor the data captured for an Amazon SageMaker AI Endpoint.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 7565 def create_monitoring_schedule(params = {}, = {}) req = build_request(:create_monitoring_schedule, params) req.send_request() end |
#create_notebook_instance(params = {}) ⇒ Types::CreateNotebookInstanceOutput
Creates an SageMaker AI notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook.
In a CreateNotebookInstance
request, specify the type of ML compute
instance that you want to run. SageMaker AI launches the instance,
installs common libraries that you can use to explore datasets for
model training, and attaches an ML storage volume to the notebook
instance.
SageMaker AI also provides a set of example notebooks. Each notebook demonstrates how to use SageMaker AI with a specific algorithm or with a machine learning framework.
After receiving the request, SageMaker AI does the following:
Creates a network interface in the SageMaker AI VPC.
(Option) If you specified
SubnetId
, SageMaker AI creates a network interface in your own VPC, which is inferred from the subnet ID that you provide in the input. When creating this network interface, SageMaker AI attaches the security group that you specified in the request to the network interface that it creates in your VPC.Launches an EC2 instance of the type specified in the request in the SageMaker AI VPC. If you specified
SubnetId
of your VPC, SageMaker AI specifies both network interfaces when launching this instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security groups allow it.
After creating the notebook instance, SageMaker AI returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it.
After SageMaker AI creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating SageMaker AI endpoints, and validate hosted models.
For more information, see How It Works.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 7791 def create_notebook_instance(params = {}, = {}) req = build_request(:create_notebook_instance, params) req.send_request() end |
#create_notebook_instance_lifecycle_config(params = {}) ⇒ Types::CreateNotebookInstanceLifecycleConfigOutput
Creates a lifecycle configuration that you can associate with a notebook instance. A lifecycle configuration is a collection of shell scripts that run when you create or start a notebook instance.
Each lifecycle configuration script has a limit of 16384 characters.
The value of the $PATH
environment variable that is available to
both scripts is /sbin:bin:/usr/sbin:/usr/bin
.
View Amazon CloudWatch Logs for notebook instance lifecycle
configurations in log group /aws/sagemaker/NotebookInstances
in log
stream [notebook-instance-name]/[LifecycleConfigHook]
.
Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 7860 def create_notebook_instance_lifecycle_config(params = {}, = {}) req = build_request(:create_notebook_instance_lifecycle_config, params) req.send_request() end |
#create_optimization_job(params = {}) ⇒ Types::CreateOptimizationJobResponse
Creates a job that optimizes a model for inference performance. To create the job, you provide the location of a source model, and you provide the settings for the optimization techniques that you want the job to apply. When the job completes successfully, SageMaker uploads the new optimized model to the output destination that you specify.
For more information about how to use this action, and about the supported optimization techniques, see Optimize model inference with Amazon SageMaker.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 8030 def create_optimization_job(params = {}, = {}) req = build_request(:create_optimization_job, params) req.send_request() end |
#create_partner_app(params = {}) ⇒ Types::CreatePartnerAppResponse
Creates an Amazon SageMaker Partner AI App.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 8117 def create_partner_app(params = {}, = {}) req = build_request(:create_partner_app, params) req.send_request() end |
#create_partner_app_presigned_url(params = {}) ⇒ Types::CreatePartnerAppPresignedUrlResponse
Creates a presigned URL to access an Amazon SageMaker Partner AI App.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 8155 def create_partner_app_presigned_url(params = {}, = {}) req = build_request(:create_partner_app_presigned_url, params) req.send_request() end |
#create_pipeline(params = {}) ⇒ Types::CreatePipelineResponse
Creates a pipeline using a JSON pipeline definition.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 8240 def create_pipeline(params = {}, = {}) req = build_request(:create_pipeline, params) req.send_request() end |
#create_presigned_domain_url(params = {}) ⇒ Types::CreatePresignedDomainUrlResponse
Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to the domain, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System volume. This operation can only be called when the authentication mode equals IAM.
The IAM role or user passed to this API defines the permissions to access the app. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the app.
You can restrict access to this API and to the URL that it returns to a list of IP addresses, Amazon VPCs or Amazon VPC Endpoints that you specify. For more information, see Connect to Amazon SageMaker AI Studio Through an Interface VPC Endpoint .
CreatePresignedDomainUrl
has a
default timeout of 5 minutes. You can configure this value using
ExpiresInSeconds
. If you try to use the URL after the timeout
limit expires, you are directed to the Amazon Web Services console
sign-in page.
- The JupyterLab session default expiration time is 12 hours. You can configure this value using SessionExpirationDurationInSeconds.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 8342 def create_presigned_domain_url(params = {}, = {}) req = build_request(:create_presigned_domain_url, params) req.send_request() end |
#create_presigned_mlflow_tracking_server_url(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 8385 def create_presigned_mlflow_tracking_server_url(params = {}, = {}) req = build_request(:create_presigned_mlflow_tracking_server_url, params) req.send_request() end |
#create_presigned_notebook_instance_url(params = {}) ⇒ Types::CreatePresignedNotebookInstanceUrlOutput
Returns a URL that you can use to connect to the Jupyter server from a
notebook instance. In the SageMaker AI console, when you choose Open
next to a notebook instance, SageMaker AI opens a new tab showing the
Jupyter server home page from the notebook instance. The console uses
this API to get the URL and show the page.
The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance.
You can restrict access to this API and to the URL that it returns to
a list of IP addresses that you specify. Use the NotIpAddress
condition operator and the aws:SourceIP
condition context key to
specify the list of IP addresses that you want to have access to the
notebook instance. For more information, see Limit Access to a
Notebook Instance by IP Address.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 8447 def create_presigned_notebook_instance_url(params = {}, = {}) req = build_request(:create_presigned_notebook_instance_url, params) req.send_request() end |
#create_processing_job(params = {}) ⇒ Types::CreateProcessingJobResponse
Creates a processing job.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 8631 def create_processing_job(params = {}, = {}) req = build_request(:create_processing_job, params) req.send_request() end |
#create_project(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 8705 def create_project(params = {}, = {}) req = build_request(:create_project, params) req.send_request() end |
#create_space(params = {}) ⇒ Types::CreateSpaceResponse
Creates a private space or a space used for real time collaboration in a domain.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 8851 def create_space(params = {}, = {}) req = build_request(:create_space, params) req.send_request() end |
#create_studio_lifecycle_config(params = {}) ⇒ Types::CreateStudioLifecycleConfigResponse
Creates a new Amazon SageMaker AI Studio Lifecycle Configuration.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 8900 def create_studio_lifecycle_config(params = {}, = {}) req = build_request(:create_studio_lifecycle_config, params) req.send_request() end |
#create_training_job(params = {}) ⇒ Types::CreateTrainingJobResponse
Starts a model training job. After training completes, SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.
If you choose to host your model using SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than SageMaker, provided that you know how to use them for inference.
In the request body, you provide the following:
AlgorithmSpecification
- Identifies the training algorithm to use.HyperParameters
- Specify these algorithm-specific parameters to enable the estimation of model parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms.Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.
InputDataConfig
- Describes the input required by the training job and the Amazon S3, EFS, or FSx location where it is stored.OutputDataConfig
- Identifies the Amazon S3 bucket where you want SageMaker to save the results of model training.ResourceConfig
- Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance.EnableManagedSpotTraining
- Optimize the cost of training machine learning models by up to 80% by using Amazon EC2 Spot instances. For more information, see Managed Spot Training.RoleArn
- The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that SageMaker can successfully complete model training.StoppingCondition
- To help cap training costs, useMaxRuntimeInSeconds
to set a time limit for training. UseMaxWaitTimeInSeconds
to specify how long a managed spot training job has to complete.Environment
- The environment variables to set in the Docker container.RetryStrategy
- The number of times to retry the job when the job fails due to anInternalServerError
.
For more information about SageMaker, see How It Works.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9388 def create_training_job(params = {}, = {}) req = build_request(:create_training_job, params) req.send_request() end |
#create_training_plan(params = {}) ⇒ Types::CreateTrainingPlanResponse
Creates a new training plan in SageMaker to reserve compute capacity.
Amazon SageMaker Training Plan is a capability within SageMaker that allows customers to reserve and manage GPU capacity for large-scale AI model training. It provides a way to secure predictable access to computational resources within specific timelines and budgets, without the need to manage underlying infrastructure.
How it works
Plans can be created for specific resources such as SageMaker Training Jobs or SageMaker HyperPod clusters, automatically provisioning resources, setting up infrastructure, executing workloads, and handling infrastructure failures.
Plan creation workflow
Users search for available plan offerings based on their requirements (e.g., instance type, count, start time, duration) using the
SearchTrainingPlanOfferings
API operation.They create a plan that best matches their needs using the ID of the plan offering they want to use.
After successful upfront payment, the plan's status becomes
Scheduled
.The plan can be used to:
Queue training jobs.
Allocate to an instance group of a SageMaker HyperPod cluster.
When the plan start date arrives, it becomes
Active
. Based on available reserved capacity:Training jobs are launched.
Instance groups are provisioned.
Plan composition
A plan can consist of one or more Reserved Capacities, each defined by
a specific instance type, quantity, Availability Zone, duration, and
start and end times. For more information about Reserved Capacity, see
ReservedCapacitySummary
.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9474 def create_training_plan(params = {}, = {}) req = build_request(:create_training_plan, params) req.send_request() end |
#create_transform_job(params = {}) ⇒ Types::CreateTransformJobResponse
Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify.
To perform batch transformations, you create a transform job and use the data that you have readily available.
In the request body, you provide the following:
TransformJobName
- Identifies the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.ModelName
- Identifies the model to use.ModelName
must be the name of an existing Amazon SageMaker model in the same Amazon Web Services Region and Amazon Web Services account. For information on creating a model, see CreateModel.TransformInput
- Describes the dataset to be transformed and the Amazon S3 location where it is stored.TransformOutput
- Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.TransformResources
- Identifies the ML compute instances for the transform job.
For more information about how batch transformation works, see Batch Transform.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9708 def create_transform_job(params = {}, = {}) req = build_request(:create_transform_job, params) req.send_request() end |
#create_trial(params = {}) ⇒ Types::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.
When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to a trial and then use the Search API to search for the tags.
To get a list of all your trials, call the ListTrials API. To view a trial's properties, call the DescribeTrial API. To create a trial component, call the CreateTrialComponent API.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9790 def create_trial(params = {}, = {}) req = build_request(:create_trial, params) req.send_request() end |
#create_trial_component(params = {}) ⇒ Types::CreateTrialComponentResponse
Creates a trial component, which is a stage of a machine learning trial. A trial is composed of one or more trial components. A trial component can be used in multiple trials.
Trial components include pre-processing jobs, training jobs, and batch transform jobs.
When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to a trial component and then use the Search API to search for the tags.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9916 def create_trial_component(params = {}, = {}) req = build_request(:create_trial_component, params) req.send_request() end |
#create_user_profile(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10189 def create_user_profile(params = {}, = {}) req = build_request(:create_user_profile, params) req.send_request() end |
#create_workforce(params = {}) ⇒ Types::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.
If you want to create a new workforce in an Amazon Web Services Region
where a workforce already exists, use the DeleteWorkforce API
operation to delete the existing workforce and then use
CreateWorkforce
to create a new workforce.
To create a private workforce using Amazon Cognito, you must specify a
Cognito user pool in CognitoConfig
. You can also create an Amazon
Cognito workforce using the Amazon SageMaker console. For more
information, see Create a Private Workforce (Amazon Cognito).
To create a private workforce using your own OIDC Identity Provider
(IdP), specify your IdP configuration in OidcConfig
. Your OIDC IdP
must support groups because groups are used by Ground Truth and
Amazon A2I to create work teams. For more information, see Create a
Private Workforce (OIDC IdP).
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10309 def create_workforce(params = {}, = {}) req = build_request(:create_workforce, params) req.send_request() end |
#create_workteam(params = {}) ⇒ Types::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.
You cannot create more than 25 work teams in an account and region.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10426 def create_workteam(params = {}, = {}) req = build_request(:create_workteam, params) req.send_request() end |
#delete_action(params = {}) ⇒ Types::DeleteActionResponse
Deletes an action.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10454 def delete_action(params = {}, = {}) req = build_request(:delete_action, params) req.send_request() end |
#delete_algorithm(params = {}) ⇒ Struct
Removes the specified algorithm from your account.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10476 def delete_algorithm(params = {}, = {}) req = build_request(:delete_algorithm, params) req.send_request() end |
#delete_app(params = {}) ⇒ Struct
Used to stop and delete an app.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10516 def delete_app(params = {}, = {}) req = build_request(:delete_app, params) req.send_request() end |
#delete_app_image_config(params = {}) ⇒ Struct
Deletes an AppImageConfig.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10538 def delete_app_image_config(params = {}, = {}) req = build_request(:delete_app_image_config, params) req.send_request() end |
#delete_artifact(params = {}) ⇒ Types::DeleteArtifactResponse
Deletes an artifact. Either ArtifactArn
or Source
must be
specified.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10579 def delete_artifact(params = {}, = {}) req = build_request(:delete_artifact, params) req.send_request() end |
#delete_association(params = {}) ⇒ Types::DeleteAssociationResponse
Deletes an association.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10613 def delete_association(params = {}, = {}) req = build_request(:delete_association, params) req.send_request() end |
#delete_cluster(params = {}) ⇒ Types::DeleteClusterResponse
Delete a SageMaker HyperPod cluster.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10642 def delete_cluster(params = {}, = {}) req = build_request(:delete_cluster, params) req.send_request() end |
#delete_cluster_scheduler_config(params = {}) ⇒ Struct
Deletes the cluster policy of the cluster.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10664 def delete_cluster_scheduler_config(params = {}, = {}) req = build_request(:delete_cluster_scheduler_config, params) req.send_request() end |
#delete_code_repository(params = {}) ⇒ Struct
Deletes the specified Git repository from your account.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10686 def delete_code_repository(params = {}, = {}) req = build_request(:delete_code_repository, params) req.send_request() end |
#delete_compilation_job(params = {}) ⇒ Struct
Deletes the specified compilation job. This action deletes only the compilation job resource in Amazon SageMaker AI. It doesn't delete other resources that are related to that job, such as the model artifacts that the job creates, the compilation logs in CloudWatch, the compiled model, or the IAM role.
You can delete a compilation job only if its current status is
COMPLETED
, FAILED
, or STOPPED
. If the job status is STARTING
or INPROGRESS
, stop the job, and then delete it after its status
becomes STOPPED
.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10717 def delete_compilation_job(params = {}, = {}) req = build_request(:delete_compilation_job, params) req.send_request() end |
#delete_compute_quota(params = {}) ⇒ Struct
Deletes the compute allocation from the cluster.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10739 def delete_compute_quota(params = {}, = {}) req = build_request(:delete_compute_quota, params) req.send_request() end |
#delete_context(params = {}) ⇒ Types::DeleteContextResponse
Deletes an context.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10767 def delete_context(params = {}, = {}) req = build_request(:delete_context, params) req.send_request() end |
#delete_data_quality_job_definition(params = {}) ⇒ Struct
Deletes a data quality monitoring job definition.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10789 def delete_data_quality_job_definition(params = {}, = {}) req = build_request(:delete_data_quality_job_definition, params) req.send_request() end |
#delete_device_fleet(params = {}) ⇒ Struct
Deletes a fleet.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10811 def delete_device_fleet(params = {}, = {}) req = build_request(:delete_device_fleet, params) req.send_request() end |
#delete_domain(params = {}) ⇒ Struct
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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10844 def delete_domain(params = {}, = {}) req = build_request(:delete_domain, params) req.send_request() end |
#delete_edge_deployment_plan(params = {}) ⇒ Struct
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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10867 def delete_edge_deployment_plan(params = {}, = {}) req = build_request(:delete_edge_deployment_plan, params) req.send_request() end |
#delete_edge_deployment_stage(params = {}) ⇒ Struct
Delete a stage in an edge deployment plan if (and only if) the stage is inactive.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10895 def delete_edge_deployment_stage(params = {}, = {}) req = build_request(:delete_edge_deployment_stage, params) req.send_request() end |
#delete_endpoint(params = {}) ⇒ Struct
Deletes an endpoint. SageMaker frees up all of the resources that were deployed when the endpoint was created.
SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the RevokeGrant API call.
When you delete your endpoint, SageMaker asynchronously deletes
associated endpoint resources such as KMS key grants. You might still
see these resources in your account for a few minutes after deleting
your endpoint. Do not delete or revoke the permissions for your
ExecutionRoleArn
, otherwise SageMaker cannot delete these resources.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10932 def delete_endpoint(params = {}, = {}) req = build_request(:delete_endpoint, params) req.send_request() end |
#delete_endpoint_config(params = {}) ⇒ Struct
Deletes an endpoint configuration. The DeleteEndpointConfig
API
deletes only the specified configuration. It does not delete endpoints
created using the configuration.
You must not delete an EndpointConfig
in use by an endpoint that is
live or while the UpdateEndpoint
or CreateEndpoint
operations are
being performed on the endpoint. If you delete the EndpointConfig
of
an endpoint that is active or being created or updated you may lose
visibility into the instance type the endpoint is using. The endpoint
must be deleted in order to stop incurring charges.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10963 def delete_endpoint_config(params = {}, = {}) req = build_request(:delete_endpoint_config, params) req.send_request() end |
#delete_experiment(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10997 def delete_experiment(params = {}, = {}) req = build_request(:delete_experiment, params) req.send_request() end |
#delete_feature_group(params = {}) ⇒ Struct
Delete the FeatureGroup
and any data that was written to the
OnlineStore
of the FeatureGroup
. Data cannot be accessed from the
OnlineStore
immediately after DeleteFeatureGroup
is called.
Data written into the OfflineStore
will not be deleted. The Amazon
Web Services Glue database and tables that are automatically created
for your OfflineStore
are not deleted.
Note that it can take approximately 10-15 minutes to delete an
OnlineStore
FeatureGroup
with the InMemory
StorageType
.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11030 def delete_feature_group(params = {}, = {}) req = build_request(:delete_feature_group, params) req.send_request() end |
#delete_flow_definition(params = {}) ⇒ Struct
Deletes the specified flow definition.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11052 def delete_flow_definition(params = {}, = {}) req = build_request(:delete_flow_definition, params) req.send_request() end |
#delete_hub(params = {}) ⇒ Struct
Delete a hub.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11074 def delete_hub(params = {}, = {}) req = build_request(:delete_hub, params) req.send_request() end |
#delete_hub_content(params = {}) ⇒ Struct
Delete the contents of a hub.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11108 def delete_hub_content(params = {}, = {}) req = build_request(:delete_hub_content, params) req.send_request() end |
#delete_hub_content_reference(params = {}) ⇒ Struct
Delete a hub content reference in order to remove a model from a private hub.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11140 def delete_hub_content_reference(params = {}, = {}) req = build_request(:delete_hub_content_reference, params) req.send_request() end |
#delete_human_task_ui(params = {}) ⇒ Struct
Use this operation to delete a human task user interface (worker task template).
To see a list of human task user interfaces (work task templates) in
your account, use ListHumanTaskUis. When you delete a worker task
template, it no longer appears when you call ListHumanTaskUis
.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11172 def delete_human_task_ui(params = {}, = {}) req = build_request(:delete_human_task_ui, params) req.send_request() end |
#delete_hyper_parameter_tuning_job(params = {}) ⇒ Struct
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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11198 def delete_hyper_parameter_tuning_job(params = {}, = {}) req = build_request(:delete_hyper_parameter_tuning_job, params) req.send_request() end |
#delete_image(params = {}) ⇒ Struct
Deletes a SageMaker AI image and all versions of the image. The container images aren't deleted.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11221 def delete_image(params = {}, = {}) req = build_request(:delete_image, params) req.send_request() end |
#delete_image_version(params = {}) ⇒ Struct
Deletes a version of a SageMaker AI image. The container image the version represents isn't deleted.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11252 def delete_image_version(params = {}, = {}) req = build_request(:delete_image_version, params) req.send_request() end |
#delete_inference_component(params = {}) ⇒ Struct
Deletes an inference component.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11274 def delete_inference_component(params = {}, = {}) req = build_request(:delete_inference_component, params) req.send_request() end |
#delete_inference_experiment(params = {}) ⇒ Types::DeleteInferenceExperimentResponse
Deletes an inference experiment.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11308 def delete_inference_experiment(params = {}, = {}) req = build_request(:delete_inference_experiment, params) req.send_request() end |
#delete_mlflow_tracking_server(params = {}) ⇒ Types::DeleteMlflowTrackingServerResponse
Deletes an MLflow Tracking Server. For more information, see Clean up MLflow resources.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11341 def delete_mlflow_tracking_server(params = {}, = {}) req = build_request(:delete_mlflow_tracking_server, params) req.send_request() end |
#delete_model(params = {}) ⇒ Struct
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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11366 def delete_model(params = {}, = {}) req = build_request(:delete_model, params) req.send_request() end |
#delete_model_bias_job_definition(params = {}) ⇒ Struct
Deletes an Amazon SageMaker AI model bias job definition.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11388 def delete_model_bias_job_definition(params = {}, = {}) req = build_request(:delete_model_bias_job_definition, params) req.send_request() end |
#delete_model_card(params = {}) ⇒ Struct
Deletes an Amazon SageMaker Model Card.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11410 def delete_model_card(params = {}, = {}) req = build_request(:delete_model_card, params) req.send_request() end |
#delete_model_explainability_job_definition(params = {}) ⇒ Struct
Deletes an Amazon SageMaker AI model explainability job definition.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11432 def delete_model_explainability_job_definition(params = {}, = {}) req = build_request(:delete_model_explainability_job_definition, params) req.send_request() end |
#delete_model_package(params = {}) ⇒ Struct
Deletes a model package.
A model package is used to create SageMaker models or list on Amazon Web Services Marketplace. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11462 def delete_model_package(params = {}, = {}) req = build_request(:delete_model_package, params) req.send_request() end |
#delete_model_package_group(params = {}) ⇒ Struct
Deletes the specified model group.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11484 def delete_model_package_group(params = {}, = {}) req = build_request(:delete_model_package_group, params) req.send_request() end |
#delete_model_package_group_policy(params = {}) ⇒ Struct
Deletes a model group resource policy.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11506 def delete_model_package_group_policy(params = {}, = {}) req = build_request(:delete_model_package_group_policy, params) req.send_request() end |
#delete_model_quality_job_definition(params = {}) ⇒ Struct
Deletes the secified model quality monitoring job definition.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11528 def delete_model_quality_job_definition(params = {}, = {}) req = build_request(:delete_model_quality_job_definition, params) req.send_request() end |
#delete_monitoring_schedule(params = {}) ⇒ Struct
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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11552 def delete_monitoring_schedule(params = {}, = {}) req = build_request(:delete_monitoring_schedule, params) req.send_request() end |
#delete_notebook_instance(params = {}) ⇒ Struct
Deletes an SageMaker AI notebook instance. Before you can delete a
notebook instance, you must call the StopNotebookInstance
API.
When you delete a notebook instance, you lose all of your data. SageMaker AI removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11580 def delete_notebook_instance(params = {}, = {}) req = build_request(:delete_notebook_instance, params) req.send_request() end |
#delete_notebook_instance_lifecycle_config(params = {}) ⇒ Struct
Deletes a notebook instance lifecycle configuration.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11602 def delete_notebook_instance_lifecycle_config(params = {}, = {}) req = build_request(:delete_notebook_instance_lifecycle_config, params) req.send_request() end |
#delete_optimization_job(params = {}) ⇒ Struct
Deletes an optimization job.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11624 def delete_optimization_job(params = {}, = {}) req = build_request(:delete_optimization_job, params) req.send_request() end |
#delete_partner_app(params = {}) ⇒ Types::DeletePartnerAppResponse
Deletes a SageMaker Partner AI App.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11660 def delete_partner_app(params = {}, = {}) req = build_request(:delete_partner_app, params) req.send_request() end |
#delete_pipeline(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11700 def delete_pipeline(params = {}, = {}) req = build_request(:delete_pipeline, params) req.send_request() end |
#delete_project(params = {}) ⇒ Struct
Delete the specified project.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11722 def delete_project(params = {}, = {}) req = build_request(:delete_project, params) req.send_request() end |
#delete_space(params = {}) ⇒ Struct
Used to delete a space.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11748 def delete_space(params = {}, = {}) req = build_request(:delete_space, params) req.send_request() end |
#delete_studio_lifecycle_config(params = {}) ⇒ Struct
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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11775 def delete_studio_lifecycle_config(params = {}, = {}) req = build_request(:delete_studio_lifecycle_config, params) req.send_request() end |
#delete_tags(params = {}) ⇒ Struct
Deletes the specified tags from an SageMaker resource.
To list a resource's tags, use the ListTags
API.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11816 def (params = {}, = {}) req = build_request(:delete_tags, params) req.send_request() end |
#delete_trial(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11850 def delete_trial(params = {}, = {}) req = build_request(:delete_trial, params) req.send_request() end |
#delete_trial_component(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11885 def delete_trial_component(params = {}, = {}) req = build_request(:delete_trial_component, params) req.send_request() end |
#delete_user_profile(params = {}) ⇒ Struct
Deletes a user profile. When a user profile is deleted, the user loses access to their EFS volume, including data, notebooks, and other artifacts.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11913 def delete_user_profile(params = {}, = {}) req = build_request(:delete_user_profile, params) req.send_request() end |
#delete_workforce(params = {}) ⇒ Struct
Use this operation to delete a workforce.
If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use this operation to delete the existing workforce and then use CreateWorkforce to create a new workforce.
If a private workforce contains one or more work teams, you must use
the DeleteWorkteam operation to delete all work teams before you
delete the workforce. If you try to delete a workforce that contains
one or more work teams, you will receive a ResourceInUse
error.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11950 def delete_workforce(params = {}, = {}) req = build_request(:delete_workforce, params) req.send_request() end |
#delete_workteam(params = {}) ⇒ Types::DeleteWorkteamResponse
Deletes an existing work team. This operation can't be undone.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11978 def delete_workteam(params = {}, = {}) req = build_request(:delete_workteam, params) req.send_request() end |
#deregister_devices(params = {}) ⇒ Struct
Deregisters the specified devices. After you deregister a device, you will need to re-register the devices.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 12005 def deregister_devices(params = {}, = {}) req = build_request(:deregister_devices, params) req.send_request() end |
#describe_action(params = {}) ⇒ Types::DescribeActionResponse
Describes an action.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 12073 def describe_action(params = {}, = {}) req = build_request(:describe_action, params) req.send_request() end |
#describe_algorithm(params = {}) ⇒ Types::DescribeAlgorithmOutput
Returns a description of the specified algorithm that is in your account.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 12256 def describe_algorithm(params = {}, = {}) req = build_request(:describe_algorithm, params) req.send_request() end |
#describe_app(params = {}) ⇒ Types::DescribeAppResponse
Describes the app.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 12329 def describe_app(params = {}, = {}) req = build_request(:describe_app, params) req.send_request() end |
#describe_app_image_config(params = {}) ⇒ Types::DescribeAppImageConfigResponse
Describes an AppImageConfig.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 12390 def describe_app_image_config(params = {}, = {}) req = build_request(:describe_app_image_config, params) req.send_request() end |
#describe_artifact(params = {}) ⇒ Types::DescribeArtifactResponse
Describes an artifact.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 12455 def describe_artifact(params = {}, = {}) req = build_request(:describe_artifact, params) req.send_request() end |
#describe_auto_ml_job(params = {}) ⇒ Types::DescribeAutoMLJobResponse
Returns information about an AutoML job created by calling CreateAutoMLJob.
DescribeAutoMLJob
.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 12596 def describe_auto_ml_job(params = {}, = {}) req = build_request(:describe_auto_ml_job, params) req.send_request() end |
#describe_auto_ml_job_v2(params = {}) ⇒ Types::DescribeAutoMLJobV2Response
Returns information about an AutoML job created by calling CreateAutoMLJobV2 or CreateAutoMLJob.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 12775 def describe_auto_ml_job_v2(params = {}, = {}) req = build_request(:describe_auto_ml_job_v2, params) req.send_request() end |
#describe_cluster(params = {}) ⇒ Types::DescribeClusterResponse
Retrieves information of a SageMaker HyperPod cluster.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 12842 def describe_cluster(params = {}, = {}) req = build_request(:describe_cluster, params) req.send_request() end |
#describe_cluster_node(params = {}) ⇒ Types::DescribeClusterNodeResponse
Retrieves information of a node (also called a instance interchangeably) of a SageMaker HyperPod cluster.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 12895 def describe_cluster_node(params = {}, = {}) req = build_request(:describe_cluster_node, params) req.send_request() end |
#describe_cluster_scheduler_config(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 12966 def describe_cluster_scheduler_config(params = {}, = {}) req = build_request(:describe_cluster_scheduler_config, params) req.send_request() end |
#describe_code_repository(params = {}) ⇒ Types::DescribeCodeRepositoryOutput
Gets details about the specified Git repository.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 13004 def describe_code_repository(params = {}, = {}) req = build_request(:describe_code_repository, params) req.send_request() end |
#describe_compilation_job(params = {}) ⇒ Types::DescribeCompilationJobResponse
Returns information about a model compilation job.
To create a model compilation job, use CreateCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 13089 def describe_compilation_job(params = {}, = {}) req = build_request(:describe_compilation_job, params) req.send_request() end |
#describe_compute_quota(params = {}) ⇒ Types::DescribeComputeQuotaResponse
Description of the compute allocation definition.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 13165 def describe_compute_quota(params = {}, = {}) req = build_request(:describe_compute_quota, params) req.send_request() end |
#describe_context(params = {}) ⇒ Types::DescribeContextResponse
Describes a context.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 13226 def describe_context(params = {}, = {}) req = build_request(:describe_context, params) req.send_request() end |
#describe_data_quality_job_definition(params = {}) ⇒ Types::DescribeDataQualityJobDefinitionResponse
Gets the details of a data quality monitoring job definition.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 13319 def describe_data_quality_job_definition(params = {}, = {}) req = build_request(:describe_data_quality_job_definition, params) req.send_request() end |
#describe_device(params = {}) ⇒ Types::DescribeDeviceResponse
Describes the device.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 13379 def describe_device(params = {}, = {}) req = build_request(:describe_device, params) req.send_request() end |
#describe_device_fleet(params = {}) ⇒ Types::DescribeDeviceFleetResponse
A description of the fleet the device belongs to.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 13424 def describe_device_fleet(params = {}, = {}) req = build_request(:describe_device_fleet, params) req.send_request() end |
#describe_domain(params = {}) ⇒ Types::DescribeDomainResponse
The description of the domain.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 13681 def describe_domain(params = {}, = {}) req = build_request(:describe_domain, params) req.send_request() end |
#describe_edge_deployment_plan(params = {}) ⇒ Types::DescribeEdgeDeploymentPlanResponse
Describes an edge deployment plan with deployment status per stage.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 13753 def describe_edge_deployment_plan(params = {}, = {}) req = build_request(:describe_edge_deployment_plan, params) req.send_request() end |
#describe_edge_packaging_job(params = {}) ⇒ Types::DescribeEdgePackagingJobResponse
A description of edge packaging jobs.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 13815 def describe_edge_packaging_job(params = {}, = {}) req = build_request(:describe_edge_packaging_job, params) req.send_request() end |
#describe_endpoint(params = {}) ⇒ Types::DescribeEndpointOutput
Returns the description of an endpoint.
The following waiters are defined for this operation (see #wait_until for detailed usage):
- endpoint_deleted
- endpoint_in_service
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 14022 def describe_endpoint(params = {}, = {}) req = build_request(:describe_endpoint, params) req.send_request() end |
#describe_endpoint_config(params = {}) ⇒ Types::DescribeEndpointConfigOutput
Returns the description of an endpoint configuration created using the
CreateEndpointConfig
API.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 14154 def describe_endpoint_config(params = {}, = {}) req = build_request(:describe_endpoint_config, params) req.send_request() end |
#describe_experiment(params = {}) ⇒ Types::DescribeExperimentResponse
Provides a list of an experiment's properties.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 14209 def describe_experiment(params = {}, = {}) req = build_request(:describe_experiment, params) req.send_request() end |
#describe_feature_group(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 14298 def describe_feature_group(params = {}, = {}) req = build_request(:describe_feature_group, params) req.send_request() end |
#describe_feature_metadata(params = {}) ⇒ Types::DescribeFeatureMetadataResponse
Shows the metadata for a feature within a feature group.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 14347 def (params = {}, = {}) req = build_request(:describe_feature_metadata, params) req.send_request() end |
#describe_flow_definition(params = {}) ⇒ Types::DescribeFlowDefinitionResponse
Returns information about the specified flow definition.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 14405 def describe_flow_definition(params = {}, = {}) req = build_request(:describe_flow_definition, params) req.send_request() end |
#describe_hub(params = {}) ⇒ Types::DescribeHubResponse
Describes a hub.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 14452 def describe_hub(params = {}, = {}) req = build_request(:describe_hub, params) req.send_request() end |
#describe_hub_content(params = {}) ⇒ Types::DescribeHubContentResponse
Describe the content of a hub.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 14533 def describe_hub_content(params = {}, = {}) req = build_request(:describe_hub_content, params) req.send_request() end |
#describe_human_task_ui(params = {}) ⇒ Types::DescribeHumanTaskUiResponse
Returns information about the requested human task user interface (worker task template).
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 14572 def describe_human_task_ui(params = {}, = {}) req = build_request(:describe_human_task_ui, params) req.send_request() end |
#describe_hyper_parameter_tuning_job(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 14873 def describe_hyper_parameter_tuning_job(params = {}, = {}) req = build_request(:describe_hyper_parameter_tuning_job, params) req.send_request() end |
#describe_image(params = {}) ⇒ Types::DescribeImageResponse
Describes a SageMaker AI image.
The following waiters are defined for this operation (see #wait_until for detailed usage):
- image_created
- image_deleted
- image_updated
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 14924 def describe_image(params = {}, = {}) req = build_request(:describe_image, params) req.send_request() end |
#describe_image_version(params = {}) ⇒ Types::DescribeImageVersionResponse
Describes a version of a SageMaker AI image.
The following waiters are defined for this operation (see #wait_until for detailed usage):
- image_version_created
- image_version_deleted
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 14997 def describe_image_version(params = {}, = {}) req = build_request(:describe_image_version, params) req.send_request() end |
#describe_inference_component(params = {}) ⇒ Types::DescribeInferenceComponentOutput
Returns information about an inference component.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 15068 def describe_inference_component(params = {}, = {}) req = build_request(:describe_inference_component, params) req.send_request() end |
#describe_inference_experiment(params = {}) ⇒ Types::DescribeInferenceExperimentResponse
Returns details about an inference experiment.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 15144 def describe_inference_experiment(params = {}, = {}) req = build_request(:describe_inference_experiment, params) req.send_request() end |
#describe_inference_recommendations_job(params = {}) ⇒ Types::DescribeInferenceRecommendationsJobResponse
Provides the results of the Inference Recommender job. One or more recommendation jobs are returned.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 15273 def describe_inference_recommendations_job(params = {}, = {}) req = build_request(:describe_inference_recommendations_job, params) req.send_request() end |
#describe_labeling_job(params = {}) ⇒ Types::DescribeLabelingJobResponse
Gets information about a labeling job.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 15369 def describe_labeling_job(params = {}, = {}) req = build_request(:describe_labeling_job, params) req.send_request() end |
#describe_lineage_group(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 15427 def describe_lineage_group(params = {}, = {}) req = build_request(:describe_lineage_group, params) req.send_request() end |
#describe_mlflow_tracking_server(params = {}) ⇒ Types::DescribeMlflowTrackingServerResponse
Returns information about an MLflow Tracking Server.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 15493 def describe_mlflow_tracking_server(params = {}, = {}) req = build_request(:describe_mlflow_tracking_server, params) req.send_request() end |
#describe_model(params = {}) ⇒ Types::DescribeModelOutput
Describes a model that you created using the CreateModel
API.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 15604 def describe_model(params = {}, = {}) req = build_request(:describe_model, params) req.send_request() end |
#describe_model_bias_job_definition(params = {}) ⇒ Types::DescribeModelBiasJobDefinitionResponse
Returns a description of a model bias job definition.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 15694 def describe_model_bias_job_definition(params = {}, = {}) req = build_request(:describe_model_bias_job_definition, params) req.send_request() end |
#describe_model_card(params = {}) ⇒ Types::DescribeModelCardResponse
Describes the content, creation time, and security configuration of an Amazon SageMaker Model Card.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 15758 def describe_model_card(params = {}, = {}) req = build_request(:describe_model_card, params) req.send_request() end |
#describe_model_card_export_job(params = {}) ⇒ Types::DescribeModelCardExportJobResponse
Describes an Amazon SageMaker Model Card export job.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 15805 def describe_model_card_export_job(params = {}, = {}) req = build_request(:describe_model_card_export_job, params) req.send_request() end |
#describe_model_explainability_job_definition(params = {}) ⇒ Types::DescribeModelExplainabilityJobDefinitionResponse
Returns a description of a model explainability job definition.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 15894 def describe_model_explainability_job_definition(params = {}, = {}) req = build_request(:describe_model_explainability_job_definition, params) req.send_request() end |
#describe_model_package(params = {}) ⇒ Types::DescribeModelPackageOutput
Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace.
If you provided a KMS Key ID when you created your model package, you will see the KMS Decrypt API call in your CloudTrail logs when you use this API.
To create models in SageMaker, buyers can subscribe to model packages listed on Amazon Web Services Marketplace.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16167 def describe_model_package(params = {}, = {}) req = build_request(:describe_model_package, params) req.send_request() end |
#describe_model_package_group(params = {}) ⇒ Types::DescribeModelPackageGroupOutput
Gets a description for the specified model group.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16210 def describe_model_package_group(params = {}, = {}) req = build_request(:describe_model_package_group, params) req.send_request() end |
#describe_model_quality_job_definition(params = {}) ⇒ Types::DescribeModelQualityJobDefinitionResponse
Returns a description of a model quality job definition.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16305 def describe_model_quality_job_definition(params = {}, = {}) req = build_request(:describe_model_quality_job_definition, params) req.send_request() end |
#describe_monitoring_schedule(params = {}) ⇒ Types::DescribeMonitoringScheduleResponse
Describes the schedule for a monitoring job.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16418 def describe_monitoring_schedule(params = {}, = {}) req = build_request(:describe_monitoring_schedule, params) req.send_request() end |
#describe_notebook_instance(params = {}) ⇒ Types::DescribeNotebookInstanceOutput
Returns information about a notebook instance.
The following waiters are defined for this operation (see #wait_until for detailed usage):
- notebook_instance_deleted
- notebook_instance_in_service
- notebook_instance_stopped
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16498 def describe_notebook_instance(params = {}, = {}) req = build_request(:describe_notebook_instance, params) req.send_request() end |
#describe_notebook_instance_lifecycle_config(params = {}) ⇒ Types::DescribeNotebookInstanceLifecycleConfigOutput
Returns a description of a notebook instance lifecycle configuration.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16545 def describe_notebook_instance_lifecycle_config(params = {}, = {}) req = build_request(:describe_notebook_instance_lifecycle_config, params) req.send_request() end |
#describe_optimization_job(params = {}) ⇒ Types::DescribeOptimizationJobResponse
Provides the properties of the specified optimization job.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16622 def describe_optimization_job(params = {}, = {}) req = build_request(:describe_optimization_job, params) req.send_request() end |
#describe_partner_app(params = {}) ⇒ Types::DescribePartnerAppResponse
Gets information about a SageMaker Partner AI App.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16680 def describe_partner_app(params = {}, = {}) req = build_request(:describe_partner_app, params) req.send_request() end |
#describe_pipeline(params = {}) ⇒ Types::DescribePipelineResponse
Describes the details of a pipeline.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16742 def describe_pipeline(params = {}, = {}) req = build_request(:describe_pipeline, params) req.send_request() end |
#describe_pipeline_definition_for_execution(params = {}) ⇒ Types::DescribePipelineDefinitionForExecutionResponse
Describes the details of an execution's pipeline definition.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16772 def describe_pipeline_definition_for_execution(params = {}, = {}) req = build_request(:describe_pipeline_definition_for_execution, params) req.send_request() end |
#describe_pipeline_execution(params = {}) ⇒ Types::DescribePipelineExecutionResponse
Describes the details of a pipeline execution.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16837 def describe_pipeline_execution(params = {}, = {}) req = build_request(:describe_pipeline_execution, params) req.send_request() end |
#describe_processing_job(params = {}) ⇒ Types::DescribeProcessingJobResponse
Returns a description of a processing job.
The following waiters are defined for this operation (see #wait_until for detailed usage):
- processing_job_completed_or_stopped
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16962 def describe_processing_job(params = {}, = {}) req = build_request(:describe_processing_job, params) req.send_request() end |
#describe_project(params = {}) ⇒ Types::DescribeProjectOutput
Describes the details of a project.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 17026 def describe_project(params = {}, = {}) req = build_request(:describe_project, params) req.send_request() end |
#describe_space(params = {}) ⇒ Types::DescribeSpaceResponse
Describes the space.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 17120 def describe_space(params = {}, = {}) req = build_request(:describe_space, params) req.send_request() end |
#describe_studio_lifecycle_config(params = {}) ⇒ Types::DescribeStudioLifecycleConfigResponse
Describes the Amazon SageMaker AI Studio Lifecycle Configuration.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 17159 def describe_studio_lifecycle_config(params = {}, = {}) req = build_request(:describe_studio_lifecycle_config, params) req.send_request() end |
#describe_subscribed_workteam(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 17194 def describe_subscribed_workteam(params = {}, = {}) req = build_request(:describe_subscribed_workteam, params) req.send_request() end |
#describe_training_job(params = {}) ⇒ Types::DescribeTrainingJobResponse
Returns information about a training job.
Some of the attributes below only appear if the training job
successfully starts. If the training job fails, TrainingJobStatus
is
Failed
and, depending on the FailureReason
, attributes like
TrainingStartTime
, TrainingTimeInSeconds
, TrainingEndTime
, and
BillableTimeInSeconds
may not be present in the response.
The following waiters are defined for this operation (see #wait_until for detailed usage):
- training_job_completed_or_stopped
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 17417 def describe_training_job(params = {}, = {}) req = build_request(:describe_training_job, params) req.send_request() end |
#describe_training_plan(params = {}) ⇒ Types::DescribeTrainingPlanResponse
Retrieves detailed information about a specific training plan.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 17483 def describe_training_plan(params = {}, = {}) req = build_request(:describe_training_plan, params) req.send_request() end |
#describe_transform_job(params = {}) ⇒ Types::DescribeTransformJobResponse
Returns information about a transform job.
The following waiters are defined for this operation (see #wait_until for detailed usage):
- transform_job_completed_or_stopped
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 17574 def describe_transform_job(params = {}, = {}) req = build_request(:describe_transform_job, params) req.send_request() end |
#describe_trial(params = {}) ⇒ Types::DescribeTrialResponse
Provides a list of a trial's properties.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 17634 def describe_trial(params = {}, = {}) req = build_request(:describe_trial, params) req.send_request() end |
#describe_trial_component(params = {}) ⇒ Types::DescribeTrialComponentResponse
Provides a list of a trials component's properties.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 17728 def describe_trial_component(params = {}, = {}) req = build_request(:describe_trial_component, params) req.send_request() end |
#describe_user_profile(params = {}) ⇒ Types::DescribeUserProfileResponse
Describes a user profile. For more information, see
CreateUserProfile
.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 17897 def describe_user_profile(params = {}, = {}) req = build_request(:describe_user_profile, params) req.send_request() end |
#describe_workforce(params = {}) ⇒ Types::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.
This operation applies only to private workforces.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 17962 def describe_workforce(params = {}, = {}) req = build_request(:describe_workforce, params) req.send_request() end |
#describe_workteam(params = {}) ⇒ Types::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).
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18009 def describe_workteam(params = {}, = {}) req = build_request(:describe_workteam, params) req.send_request() end |
#disable_sagemaker_servicecatalog_portfolio(params = {}) ⇒ Struct
Disables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18023 def disable_sagemaker_servicecatalog_portfolio(params = {}, = {}) req = build_request(:disable_sagemaker_servicecatalog_portfolio, params) req.send_request() end |
#disassociate_trial_component(params = {}) ⇒ Types::DisassociateTrialComponentResponse
Disassociates a trial component from a trial. This doesn't effect other trials the component is associated with. Before you can delete a component, you must disassociate the component from all trials it is associated with. To associate a trial component with a trial, call the AssociateTrialComponent API.
To get a list of the trials a component is associated with, use the
Search API. Specify ExperimentTrialComponent
for the Resource
parameter. The list appears in the response under
Results.TrialComponent.Parents
.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18071 def disassociate_trial_component(params = {}, = {}) req = build_request(:disassociate_trial_component, params) req.send_request() end |
#enable_sagemaker_servicecatalog_portfolio(params = {}) ⇒ Struct
Enables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18085 def enable_sagemaker_servicecatalog_portfolio(params = {}, = {}) req = build_request(:enable_sagemaker_servicecatalog_portfolio, params) req.send_request() end |
#get_device_fleet_report(params = {}) ⇒ Types::GetDeviceFleetReportResponse
Describes a fleet.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18139 def get_device_fleet_report(params = {}, = {}) req = build_request(:get_device_fleet_report, params) req.send_request() end |
#get_lineage_group_policy(params = {}) ⇒ Types::GetLineageGroupPolicyResponse
The resource policy for the lineage group.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18169 def get_lineage_group_policy(params = {}, = {}) req = build_request(:get_lineage_group_policy, params) req.send_request() end |
#get_model_package_group_policy(params = {}) ⇒ Types::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..
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18204 def get_model_package_group_policy(params = {}, = {}) req = build_request(:get_model_package_group_policy, params) req.send_request() end |
#get_sagemaker_servicecatalog_portfolio_status(params = {}) ⇒ Types::GetSagemakerServicecatalogPortfolioStatusOutput
Gets the status of Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18224 def get_sagemaker_servicecatalog_portfolio_status(params = {}, = {}) req = build_request(:get_sagemaker_servicecatalog_portfolio_status, params) req.send_request() end |
#get_scaling_configuration_recommendation(params = {}) ⇒ Types::GetScalingConfigurationRecommendationResponse
Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job. Returns recommendations for autoscaling policies that you can apply to your SageMaker endpoint.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18308 def get_scaling_configuration_recommendation(params = {}, = {}) req = build_request(:get_scaling_configuration_recommendation, params) req.send_request() end |
#get_search_suggestions(params = {}) ⇒ Types::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
.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18348 def get_search_suggestions(params = {}, = {}) req = build_request(:get_search_suggestions, params) req.send_request() end |
#import_hub_content(params = {}) ⇒ Types::ImportHubContentResponse
Import hub content.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18429 def import_hub_content(params = {}, = {}) req = build_request(:import_hub_content, params) req.send_request() end |
#list_actions(params = {}) ⇒ Types::ListActionsResponse
Lists the actions in your account and their properties.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18503 def list_actions(params = {}, = {}) req = build_request(:list_actions, params) req.send_request() end |
#list_algorithms(params = {}) ⇒ Types::ListAlgorithmsOutput
Lists the machine learning algorithms that have been created.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18570 def list_algorithms(params = {}, = {}) req = build_request(:list_algorithms, params) req.send_request() end |
#list_aliases(params = {}) ⇒ Types::ListAliasesResponse
Lists the aliases of a specified image or image version.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18622 def list_aliases(params = {}, = {}) req = build_request(:list_aliases, params) req.send_request() end |
#list_app_image_configs(params = {}) ⇒ Types::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.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18728 def list_app_image_configs(params = {}, = {}) req = build_request(:list_app_image_configs, params) req.send_request() end |
#list_apps(params = {}) ⇒ Types::ListAppsResponse
Lists apps.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18806 def list_apps(params = {}, = {}) req = build_request(:list_apps, params) req.send_request() end |
#list_artifacts(params = {}) ⇒ Types::ListArtifactsResponse
Lists the artifacts in your account and their properties.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18881 def list_artifacts(params = {}, = {}) req = build_request(:list_artifacts, params) req.send_request() end |
#list_associations(params = {}) ⇒ Types::ListAssociationsResponse
Lists the associations in your account and their properties.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18976 def list_associations(params = {}, = {}) req = build_request(:list_associations, params) req.send_request() end |
#list_auto_ml_jobs(params = {}) ⇒ Types::ListAutoMLJobsResponse
Request a list of jobs.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 19055 def list_auto_ml_jobs(params = {}, = {}) req = build_request(:list_auto_ml_jobs, params) req.send_request() end |
#list_candidates_for_auto_ml_job(params = {}) ⇒ Types::ListCandidatesForAutoMLJobResponse
List the candidates created for the job.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 19147 def list_candidates_for_auto_ml_job(params = {}, = {}) req = build_request(:list_candidates_for_auto_ml_job, params) req.send_request() end |
#list_cluster_nodes(params = {}) ⇒ Types::ListClusterNodesResponse
Retrieves the list of instances (also called nodes interchangeably) in a SageMaker HyperPod cluster.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 19250 def list_cluster_nodes(params = {}, = {}) req = build_request(:list_cluster_nodes, params) req.send_request() end |
#list_cluster_scheduler_configs(params = {}) ⇒ Types::ListClusterSchedulerConfigsResponse
List the cluster policy configurations.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 19338 def list_cluster_scheduler_configs(params = {}, = {}) req = build_request(:list_cluster_scheduler_configs, params) req.send_request() end |
#list_clusters(params = {}) ⇒ Types::ListClustersResponse
Retrieves the list of SageMaker HyperPod clusters.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 19442 def list_clusters(params = {}, = {}) req = build_request(:list_clusters, params) req.send_request() end |
#list_code_repositories(params = {}) ⇒ Types::ListCodeRepositoriesOutput
Gets a list of the Git repositories in your account.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 19520 def list_code_repositories(params = {}, = {}) req = build_request(:list_code_repositories, params) req.send_request() end |
#list_compilation_jobs(params = {}) ⇒ Types::ListCompilationJobsResponse
Lists model compilation jobs that satisfy various filters.
To create a model compilation job, use CreateCompilationJob. To get information about a particular model compilation job you have created, use DescribeCompilationJob.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 19617 def list_compilation_jobs(params = {}, = {}) req = build_request(:list_compilation_jobs, params) req.send_request() end |
#list_compute_quotas(params = {}) ⇒ Types::ListComputeQuotasResponse
List the resource allocation definitions.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 19714 def list_compute_quotas(params = {}, = {}) req = build_request(:list_compute_quotas, params) req.send_request() end |
#list_contexts(params = {}) ⇒ Types::ListContextsResponse
Lists the contexts in your account and their properties.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 19788 def list_contexts(params = {}, = {}) req = build_request(:list_contexts, params) req.send_request() end |
#list_data_quality_job_definitions(params = {}) ⇒ Types::ListDataQualityJobDefinitionsResponse
Lists the data quality job definitions in your account.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 19861 def list_data_quality_job_definitions(params = {}, = {}) req = build_request(:list_data_quality_job_definitions, params) req.send_request() end |
#list_device_fleets(params = {}) ⇒ Types::ListDeviceFleetsResponse
Returns a list of devices in the fleet.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 19931 def list_device_fleets(params = {}, = {}) req = build_request(:list_device_fleets, params) req.send_request() end |
#list_devices(params = {}) ⇒ Types::ListDevicesResponse
A list of devices.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 19992 def list_devices(params = {}, = {}) req = build_request(:list_devices, params) req.send_request() end |
#list_domains(params = {}) ⇒ Types::ListDomainsResponse
Lists the domains.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 20042 def list_domains(params = {}, = {}) req = build_request(:list_domains, params) req.send_request() end |
#list_edge_deployment_plans(params = {}) ⇒ Types::ListEdgeDeploymentPlansResponse
Lists all edge deployment plans.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 20121 def list_edge_deployment_plans(params = {}, = {}) req = build_request(:list_edge_deployment_plans, params) req.send_request() end |
#list_edge_packaging_jobs(params = {}) ⇒ Types::ListEdgePackagingJobsResponse
Returns a list of edge packaging jobs.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 20202 def list_edge_packaging_jobs(params = {}, = {}) req = build_request(:list_edge_packaging_jobs, params) req.send_request() end |
#list_endpoint_configs(params = {}) ⇒ Types::ListEndpointConfigsOutput
Lists endpoint configurations.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 20266 def list_endpoint_configs(params = {}, = {}) req = build_request(:list_endpoint_configs, params) req.send_request() end |
#list_endpoints(params = {}) ⇒ Types::ListEndpointsOutput
Lists endpoints.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 20347 def list_endpoints(params = {}, = {}) req = build_request(:list_endpoints, params) req.send_request() end |
#list_experiments(params = {}) ⇒ Types::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.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 20414 def list_experiments(params = {}, = {}) req = build_request(:list_experiments, params) req.send_request() end |
#list_feature_groups(params = {}) ⇒ Types::ListFeatureGroupsResponse
List FeatureGroup
s based on given filter and order.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 20487 def list_feature_groups(params = {}, = {}) req = build_request(:list_feature_groups, params) req.send_request() end |
#list_flow_definitions(params = {}) ⇒ Types::ListFlowDefinitionsResponse
Returns information about the flow definitions in your account.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 20546 def list_flow_definitions(params = {}, = {}) req = build_request(:list_flow_definitions, params) req.send_request() end |
#list_hub_content_versions(params = {}) ⇒ Types::ListHubContentVersionsResponse
List hub content versions.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 20634 def list_hub_content_versions(params = {}, = {}) req = build_request(:list_hub_content_versions, params) req.send_request() end |
#list_hub_contents(params = {}) ⇒ Types::ListHubContentsResponse
List the contents of a hub.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 20716 def list_hub_contents(params = {}, = {}) req = build_request(:list_hub_contents, params) req.send_request() end |
#list_hubs(params = {}) ⇒ Types::ListHubsResponse
List all existing hubs.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 20789 def list_hubs(params = {}, = {}) req = build_request(:list_hubs, params) req.send_request() end |
#list_human_task_uis(params = {}) ⇒ Types::ListHumanTaskUisResponse
Returns information about the human task user interfaces in your account.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 20847 def list_human_task_uis(params = {}, = {}) req = build_request(:list_human_task_uis, params) req.send_request() end |
#list_hyper_parameter_tuning_jobs(params = {}) ⇒ Types::ListHyperParameterTuningJobsResponse
Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 20945 def list_hyper_parameter_tuning_jobs(params = {}, = {}) req = build_request(:list_hyper_parameter_tuning_jobs, params) req.send_request() end |
#list_image_versions(params = {}) ⇒ Types::ListImageVersionsResponse
Lists the versions of a specified image and their properties. The list can be filtered by creation time or modified time.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 21025 def list_image_versions(params = {}, = {}) req = build_request(:list_image_versions, params) req.send_request() end |
#list_images(params = {}) ⇒ Types::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.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 21107 def list_images(params = {}, = {}) req = build_request(:list_images, params) req.send_request() end |
#list_inference_components(params = {}) ⇒ Types::ListInferenceComponentsOutput
Lists the inference components in your account and their properties.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 21205 def list_inference_components(params = {}, = {}) req = build_request(:list_inference_components, params) req.send_request() end |
#list_inference_experiments(params = {}) ⇒ Types::ListInferenceExperimentsResponse
Returns the list of all inference experiments.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 21302 def list_inference_experiments(params = {}, = {}) req = build_request(:list_inference_experiments, params) req.send_request() end |
#list_inference_recommendations_job_steps(params = {}) ⇒ Types::ListInferenceRecommendationsJobStepsResponse
Returns a list of the subtasks for an Inference Recommender job.
The supported subtasks are benchmarks, which evaluate the performance of your model on different instance types.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 21387 def list_inference_recommendations_job_steps(params = {}, = {}) req = build_request(:list_inference_recommendations_job_steps, params) req.send_request() end |
#list_inference_recommendations_jobs(params = {}) ⇒ Types::ListInferenceRecommendationsJobsResponse
Lists recommendation jobs that satisfy various filters.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 21486 def list_inference_recommendations_jobs(params = {}, = {}) req = build_request(:list_inference_recommendations_jobs, params) req.send_request() end |
#list_labeling_jobs(params = {}) ⇒ Types::ListLabelingJobsResponse
Gets a list of labeling jobs.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 21582 def list_labeling_jobs(params = {}, = {}) req = build_request(:list_labeling_jobs, params) req.send_request() end |
#list_labeling_jobs_for_workteam(params = {}) ⇒ Types::ListLabelingJobsForWorkteamResponse
Gets a list of labeling jobs assigned to a specified work team.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 21657 def list_labeling_jobs_for_workteam(params = {}, = {}) req = build_request(:list_labeling_jobs_for_workteam, params) req.send_request() end |
#list_lineage_groups(params = {}) ⇒ Types::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.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 21725 def list_lineage_groups(params = {}, = {}) req = build_request(:list_lineage_groups, params) req.send_request() end |
#list_mlflow_tracking_servers(params = {}) ⇒ Types::ListMlflowTrackingServersResponse
Lists all MLflow Tracking Servers.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 21812 def list_mlflow_tracking_servers(params = {}, = {}) req = build_request(:list_mlflow_tracking_servers, params) req.send_request() end |
#list_model_bias_job_definitions(params = {}) ⇒ Types::ListModelBiasJobDefinitionsResponse
Lists model bias jobs definitions that satisfy various filters.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 21882 def list_model_bias_job_definitions(params = {}, = {}) req = build_request(:list_model_bias_job_definitions, params) req.send_request() end |
#list_model_card_export_jobs(params = {}) ⇒ Types::ListModelCardExportJobsResponse
List the export jobs for the Amazon SageMaker Model Card.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 21963 def list_model_card_export_jobs(params = {}, = {}) req = build_request(:list_model_card_export_jobs, params) req.send_request() end |
#list_model_card_versions(params = {}) ⇒ Types::ListModelCardVersionsResponse
List existing versions of an Amazon SageMaker Model Card.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22035 def list_model_card_versions(params = {}, = {}) req = build_request(:list_model_card_versions, params) req.send_request() end |
#list_model_cards(params = {}) ⇒ Types::ListModelCardsResponse
List existing model cards.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22103 def list_model_cards(params = {}, = {}) req = build_request(:list_model_cards, params) req.send_request() end |
#list_model_explainability_job_definitions(params = {}) ⇒ Types::ListModelExplainabilityJobDefinitionsResponse
Lists model explainability job definitions that satisfy various filters.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22175 def list_model_explainability_job_definitions(params = {}, = {}) req = build_request(:list_model_explainability_job_definitions, params) req.send_request() end |
#list_model_metadata(params = {}) ⇒ Types::ListModelMetadataResponse
Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22234 def (params = {}, = {}) req = build_request(:list_model_metadata, params) req.send_request() end |
#list_model_package_groups(params = {}) ⇒ Types::ListModelPackageGroupsOutput
Gets a list of the model groups in your Amazon Web Services account.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22308 def list_model_package_groups(params = {}, = {}) req = build_request(:list_model_package_groups, params) req.send_request() end |
#list_model_packages(params = {}) ⇒ Types::ListModelPackagesOutput
Lists the model packages that have been created.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22400 def list_model_packages(params = {}, = {}) req = build_request(:list_model_packages, params) req.send_request() end |
#list_model_quality_job_definitions(params = {}) ⇒ Types::ListModelQualityJobDefinitionsResponse
Gets a list of model quality monitoring job definitions in your account.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22475 def list_model_quality_job_definitions(params = {}, = {}) req = build_request(:list_model_quality_job_definitions, params) req.send_request() end |
#list_models(params = {}) ⇒ Types::ListModelsOutput
Lists models created with the CreateModel
API.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22539 def list_models(params = {}, = {}) req = build_request(:list_models, params) req.send_request() end |
#list_monitoring_alert_history(params = {}) ⇒ Types::ListMonitoringAlertHistoryResponse
Gets a list of past alerts in a model monitoring schedule.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22612 def list_monitoring_alert_history(params = {}, = {}) req = build_request(:list_monitoring_alert_history, params) req.send_request() end |
#list_monitoring_alerts(params = {}) ⇒ Types::ListMonitoringAlertsResponse
Gets the alerts for a single monitoring schedule.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22661 def list_monitoring_alerts(params = {}, = {}) req = build_request(:list_monitoring_alerts, params) req.send_request() end |
#list_monitoring_executions(params = {}) ⇒ Types::ListMonitoringExecutionsResponse
Returns list of all monitoring job executions.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22765 def list_monitoring_executions(params = {}, = {}) req = build_request(:list_monitoring_executions, params) req.send_request() end |
#list_monitoring_schedules(params = {}) ⇒ Types::ListMonitoringSchedulesResponse
Returns list of all monitoring schedules.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22865 def list_monitoring_schedules(params = {}, = {}) req = build_request(:list_monitoring_schedules, params) req.send_request() end |
#list_notebook_instance_lifecycle_configs(params = {}) ⇒ Types::ListNotebookInstanceLifecycleConfigsOutput
Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22946 def list_notebook_instance_lifecycle_configs(params = {}, = {}) req = build_request(:list_notebook_instance_lifecycle_configs, params) req.send_request() end |
#list_notebook_instances(params = {}) ⇒ Types::ListNotebookInstancesOutput
Returns a list of the SageMaker AI notebook instances in the requester's account in an Amazon Web Services Region.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23060 def list_notebook_instances(params = {}, = {}) req = build_request(:list_notebook_instances, params) req.send_request() end |
#list_optimization_jobs(params = {}) ⇒ Types::ListOptimizationJobsResponse
Lists the optimization jobs in your account and their properties.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23154 def list_optimization_jobs(params = {}, = {}) req = build_request(:list_optimization_jobs, params) req.send_request() end |
#list_partner_apps(params = {}) ⇒ Types::ListPartnerAppsResponse
Lists all of the SageMaker Partner AI Apps in an account.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23202 def list_partner_apps(params = {}, = {}) req = build_request(:list_partner_apps, params) req.send_request() end |
#list_pipeline_execution_steps(params = {}) ⇒ Types::ListPipelineExecutionStepsResponse
Gets a list of PipeLineExecutionStep
objects.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23301 def list_pipeline_execution_steps(params = {}, = {}) req = build_request(:list_pipeline_execution_steps, params) req.send_request() end |
#list_pipeline_executions(params = {}) ⇒ Types::ListPipelineExecutionsResponse
Gets a list of the pipeline executions.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23367 def list_pipeline_executions(params = {}, = {}) req = build_request(:list_pipeline_executions, params) req.send_request() end |
#list_pipeline_parameters_for_execution(params = {}) ⇒ Types::ListPipelineParametersForExecutionResponse
Gets a list of parameters for a pipeline execution.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23412 def list_pipeline_parameters_for_execution(params = {}, = {}) req = build_request(:list_pipeline_parameters_for_execution, params) req.send_request() end |
#list_pipelines(params = {}) ⇒ Types::ListPipelinesResponse
Gets a list of pipelines.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23480 def list_pipelines(params = {}, = {}) req = build_request(:list_pipelines, params) req.send_request() end |
#list_processing_jobs(params = {}) ⇒ Types::ListProcessingJobsResponse
Lists processing jobs that satisfy various filters.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23563 def list_processing_jobs(params = {}, = {}) req = build_request(:list_processing_jobs, params) req.send_request() end |
#list_projects(params = {}) ⇒ Types::ListProjectsOutput
Gets a list of the projects in an Amazon Web Services account.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23630 def list_projects(params = {}, = {}) req = build_request(:list_projects, params) req.send_request() end |
#list_resource_catalogs(params = {}) ⇒ Types::ListResourceCatalogsResponse
Lists Amazon SageMaker Catalogs based on given filters and orders. The
maximum number of ResourceCatalog
s viewable is 1000.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23694 def list_resource_catalogs(params = {}, = {}) req = build_request(:list_resource_catalogs, params) req.send_request() end |
#list_spaces(params = {}) ⇒ Types::ListSpacesResponse
Lists spaces.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23764 def list_spaces(params = {}, = {}) req = build_request(:list_spaces, params) req.send_request() end |
#list_stage_devices(params = {}) ⇒ Types::ListStageDevicesResponse
Lists devices allocated to the stage, containing detailed device information and deployment status.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23825 def list_stage_devices(params = {}, = {}) req = build_request(:list_stage_devices, params) req.send_request() end |
#list_studio_lifecycle_configs(params = {}) ⇒ Types::ListStudioLifecycleConfigsResponse
Lists the Amazon SageMaker AI Studio Lifecycle Configurations in your Amazon Web Services Account.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23911 def list_studio_lifecycle_configs(params = {}, = {}) req = build_request(:list_studio_lifecycle_configs, params) req.send_request() end |
#list_subscribed_workteams(params = {}) ⇒ Types::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.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23962 def list_subscribed_workteams(params = {}, = {}) req = build_request(:list_subscribed_workteams, params) req.send_request() end |
#list_tags(params = {}) ⇒ Types::ListTagsOutput
Returns the tags for the specified SageMaker resource.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 24007 def (params = {}, = {}) req = build_request(:list_tags, params) req.send_request() end |
#list_training_jobs(params = {}) ⇒ Types::ListTrainingJobsResponse
Lists training jobs.
StatusEquals
and MaxResults
are set at the same time, the
MaxResults
number of training jobs are first retrieved ignoring the
StatusEquals
parameter and then they are filtered by the
StatusEquals
parameter, which is returned as a response.
For example, if ListTrainingJobs
is invoked with the following
parameters:
{ ... MaxResults: 100, StatusEquals: InProgress ... }
First, 100 trainings jobs with any status, including those other than
InProgress
, are selected (sorted according to the creation time,
from the most current to the oldest). Next, those with a status of
InProgress
are returned.
You can quickly test the API using the following Amazon Web Services CLI code.
aws sagemaker list-training-jobs --max-results 100 --status-equals
InProgress
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 24128 def list_training_jobs(params = {}, = {}) req = build_request(:list_training_jobs, params) req.send_request() end |
#list_training_jobs_for_hyper_parameter_tuning_job(params = {}) ⇒ Types::ListTrainingJobsForHyperParameterTuningJobResponse
Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 24207 def list_training_jobs_for_hyper_parameter_tuning_job(params = {}, = {}) req = build_request(:list_training_jobs_for_hyper_parameter_tuning_job, params) req.send_request() end |
#list_training_plans(params = {}) ⇒ Types::ListTrainingPlansResponse
Retrieves a list of training plans for the current account.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 24296 def list_training_plans(params = {}, = {}) req = build_request(:list_training_plans, params) req.send_request() end |
#list_transform_jobs(params = {}) ⇒ Types::ListTransformJobsResponse
Lists transform jobs.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 24379 def list_transform_jobs(params = {}, = {}) req = build_request(:list_transform_jobs, params) req.send_request() end |
#list_trial_components(params = {}) ⇒ Types::ListTrialComponentsResponse
Lists the trial components in your account. You can sort the list by trial component name or creation time. You can filter the list to show only components that were created in a specific time range. You can also filter on one of the following:
ExperimentName
SourceArn
TrialName
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 24487 def list_trial_components(params = {}, = {}) req = build_request(:list_trial_components, params) req.send_request() end |
#list_trials(params = {}) ⇒ Types::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.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 24564 def list_trials(params = {}, = {}) req = build_request(:list_trials, params) req.send_request() end |
#list_user_profiles(params = {}) ⇒ Types::ListUserProfilesResponse
Lists user profiles.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 24629 def list_user_profiles(params = {}, = {}) req = build_request(:list_user_profiles, params) req.send_request() end |
#list_workforces(params = {}) ⇒ Types::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.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 24707 def list_workforces(params = {}, = {}) req = build_request(:list_workforces, params) req.send_request() end |
#list_workteams(params = {}) ⇒ Types::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.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 24779 def list_workteams(params = {}, = {}) req = build_request(:list_workteams, params) req.send_request() end |
#put_model_package_group_policy(params = {}) ⇒ Types::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..
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 24818 def put_model_package_group_policy(params = {}, = {}) req = build_request(:put_model_package_group_policy, params) req.send_request() end |
#query_lineage(params = {}) ⇒ Types::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.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 24925 def query_lineage(params = {}, = {}) req = build_request(:query_lineage, params) req.send_request() end |
#register_devices(params = {}) ⇒ Struct
Register devices.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 24966 def register_devices(params = {}, = {}) req = build_request(:register_devices, params) req.send_request() end |
#render_ui_template(params = {}) ⇒ Types::RenderUiTemplateResponse
Renders the UI template so that you can preview the worker's experience.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25024 def render_ui_template(params = {}, = {}) req = build_request(:render_ui_template, params) req.send_request() end |
#retry_pipeline_execution(params = {}) ⇒ Types::RetryPipelineExecutionResponse
Retry the execution of the pipeline.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25068 def retry_pipeline_execution(params = {}, = {}) req = build_request(:retry_pipeline_execution, params) req.send_request() end |
#search(params = {}) ⇒ Types::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.
You can query against the following value types: numeric, text, Boolean, and timestamp.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25191 def search(params = {}, = {}) req = build_request(:search, params) req.send_request() end |
#search_training_plan_offerings(params = {}) ⇒ Types::SearchTrainingPlanOfferingsResponse
Searches for available training plan offerings based on specified criteria.
Users search for available plan offerings based on their requirements (e.g., instance type, count, start time, duration).
And then, they create a plan that best matches their needs using the ID of the plan offering they want to use.
For more information about how to reserve GPU capacity for your
SageMaker training jobs or SageMaker HyperPod clusters using Amazon
SageMaker Training Plan , see CreateTrainingPlan
.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25287 def search_training_plan_offerings(params = {}, = {}) req = build_request(:search_training_plan_offerings, params) req.send_request() end |
#send_pipeline_execution_step_failure(params = {}) ⇒ Types::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).
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25331 def send_pipeline_execution_step_failure(params = {}, = {}) req = build_request(:send_pipeline_execution_step_failure, params) req.send_request() end |
#send_pipeline_execution_step_success(params = {}) ⇒ Types::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).
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25380 def send_pipeline_execution_step_success(params = {}, = {}) req = build_request(:send_pipeline_execution_step_success, params) req.send_request() end |
#start_edge_deployment_stage(params = {}) ⇒ Struct
Starts a stage in an edge deployment plan.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25406 def start_edge_deployment_stage(params = {}, = {}) req = build_request(:start_edge_deployment_stage, params) req.send_request() end |
#start_inference_experiment(params = {}) ⇒ Types::StartInferenceExperimentResponse
Starts an inference experiment.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25434 def start_inference_experiment(params = {}, = {}) req = build_request(:start_inference_experiment, params) req.send_request() end |
#start_mlflow_tracking_server(params = {}) ⇒ Types::StartMlflowTrackingServerResponse
Programmatically start an MLflow Tracking Server.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25462 def start_mlflow_tracking_server(params = {}, = {}) req = build_request(:start_mlflow_tracking_server, params) req.send_request() end |
#start_monitoring_schedule(params = {}) ⇒ Struct
Starts a previously stopped monitoring schedule.
scheduled
.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25489 def start_monitoring_schedule(params = {}, = {}) req = build_request(:start_monitoring_schedule, params) req.send_request() end |
#start_notebook_instance(params = {}) ⇒ Struct
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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25515 def start_notebook_instance(params = {}, = {}) req = build_request(:start_notebook_instance, params) req.send_request() end |
#start_pipeline_execution(params = {}) ⇒ Types::StartPipelineExecutionResponse
Starts a pipeline execution.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25587 def start_pipeline_execution(params = {}, = {}) req = build_request(:start_pipeline_execution, params) req.send_request() end |
#stop_auto_ml_job(params = {}) ⇒ Struct
A method for forcing a running job to shut down.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25609 def stop_auto_ml_job(params = {}, = {}) req = build_request(:stop_auto_ml_job, params) req.send_request() end |
#stop_compilation_job(params = {}) ⇒ Struct
Stops a model compilation job.
To stop a job, Amazon SageMaker AI sends the algorithm the SIGTERM signal. This gracefully shuts the job down. If the job hasn't stopped, it sends the SIGKILL signal.
When it receives a StopCompilationJob
request, Amazon SageMaker AI
changes the CompilationJobStatus
of the job to Stopping
. After
Amazon SageMaker stops the job, it sets the CompilationJobStatus
to
Stopped
.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25640 def stop_compilation_job(params = {}, = {}) req = build_request(:stop_compilation_job, params) req.send_request() end |
#stop_edge_deployment_stage(params = {}) ⇒ Struct
Stops a stage in an edge deployment plan.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25666 def stop_edge_deployment_stage(params = {}, = {}) req = build_request(:stop_edge_deployment_stage, params) req.send_request() end |
#stop_edge_packaging_job(params = {}) ⇒ Struct
Request to stop an edge packaging job.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25688 def stop_edge_packaging_job(params = {}, = {}) req = build_request(:stop_edge_packaging_job, params) req.send_request() end |
#stop_hyper_parameter_tuning_job(params = {}) ⇒ Struct
Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched.
All model artifacts output from the training jobs are stored in Amazon
Simple Storage Service (Amazon S3). All data that the training jobs
write to Amazon CloudWatch Logs are still available in CloudWatch.
After the tuning job moves to the Stopped
state, it releases all
reserved resources for the tuning job.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25717 def stop_hyper_parameter_tuning_job(params = {}, = {}) req = build_request(:stop_hyper_parameter_tuning_job, params) req.send_request() end |
#stop_inference_experiment(params = {}) ⇒ Types::StopInferenceExperimentResponse
Stops an inference experiment.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25790 def stop_inference_experiment(params = {}, = {}) req = build_request(:stop_inference_experiment, params) req.send_request() end |
#stop_inference_recommendations_job(params = {}) ⇒ Struct
Stops an Inference Recommender job.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25812 def stop_inference_recommendations_job(params = {}, = {}) req = build_request(:stop_inference_recommendations_job, params) req.send_request() end |
#stop_labeling_job(params = {}) ⇒ Struct
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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25836 def stop_labeling_job(params = {}, = {}) req = build_request(:stop_labeling_job, params) req.send_request() end |
#stop_mlflow_tracking_server(params = {}) ⇒ Types::StopMlflowTrackingServerResponse
Programmatically stop an MLflow Tracking Server.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25864 def stop_mlflow_tracking_server(params = {}, = {}) req = build_request(:stop_mlflow_tracking_server, params) req.send_request() end |
#stop_monitoring_schedule(params = {}) ⇒ Struct
Stops a previously started monitoring schedule.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25886 def stop_monitoring_schedule(params = {}, = {}) req = build_request(:stop_monitoring_schedule, params) req.send_request() end |
#stop_notebook_instance(params = {}) ⇒ Struct
Terminates the ML compute instance. Before terminating the instance,
SageMaker AI disconnects the ML storage volume from it. SageMaker AI
preserves the ML storage volume. SageMaker AI stops charging you for
the ML compute instance when you call StopNotebookInstance
.
To access data on the ML storage volume for a notebook instance that
has been terminated, call the StartNotebookInstance
API.
StartNotebookInstance
launches another ML compute instance,
configures it, and attaches the preserved ML storage volume so you can
continue your work.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25917 def stop_notebook_instance(params = {}, = {}) req = build_request(:stop_notebook_instance, params) req.send_request() end |
#stop_optimization_job(params = {}) ⇒ Struct
Ends a running inference optimization job.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25939 def stop_optimization_job(params = {}, = {}) req = build_request(:stop_optimization_job, params) req.send_request() end |
#stop_pipeline_execution(params = {}) ⇒ Types::StopPipelineExecutionResponse
Stops a pipeline execution.
Callback Step
A pipeline execution won't stop while a callback step is running.
When you call StopPipelineExecution
on a pipeline execution with a
running callback step, SageMaker Pipelines sends an additional Amazon
SQS message to the specified SQS queue. The body of the SQS message
contains a "Status" field which is set to "Stopping".
You should add logic to your Amazon SQS message consumer to take any
needed action (for example, resource cleanup) upon receipt of the
message followed by a call to SendPipelineExecutionStepSuccess
or
SendPipelineExecutionStepFailure
.
Only when SageMaker Pipelines receives one of these calls will it stop the pipeline execution.
Lambda Step
A pipeline execution can't be stopped while a lambda step is running
because the Lambda function invoked by the lambda step can't be
stopped. If you attempt to stop the execution while the Lambda
function is running, the pipeline waits for the Lambda function to
finish or until the timeout is hit, whichever occurs first, and then
stops. If the Lambda function finishes, the pipeline execution status
is Stopped
. If the timeout is hit the pipeline execution status is
Failed
.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 26003 def stop_pipeline_execution(params = {}, = {}) req = build_request(:stop_pipeline_execution, params) req.send_request() end |
#stop_processing_job(params = {}) ⇒ Struct
Stops a processing job.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 26025 def stop_processing_job(params = {}, = {}) req = build_request(:stop_processing_job, params) req.send_request() end |
#stop_training_job(params = {}) ⇒ Struct
Stops a training job. To stop a job, SageMaker sends the algorithm the
SIGTERM
signal, which delays job termination for 120 seconds.
Algorithms might use this 120-second window to save the model
artifacts, so the results of the training is not lost.
When it receives a StopTrainingJob
request, SageMaker changes the
status of the job to Stopping
. After SageMaker stops the job, it
sets the status to Stopped
.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 26054 def stop_training_job(params = {}, = {}) req = build_request(:stop_training_job, params) req.send_request() end |
#stop_transform_job(params = {}) ⇒ Struct
Stops a batch transform job.
When Amazon SageMaker receives a StopTransformJob
request, the
status of the job changes to Stopping
. After Amazon SageMaker stops
the job, the status is set to Stopped
. When you stop a batch
transform job before it is completed, Amazon SageMaker doesn't store
the job's output in Amazon S3.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 26082 def stop_transform_job(params = {}, = {}) req = build_request(:stop_transform_job, params) req.send_request() end |
#update_action(params = {}) ⇒ Types::UpdateActionResponse
Updates an action.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 26128 def update_action(params = {}, = {}) req = build_request(:update_action, params) req.send_request() end |
#update_app_image_config(params = {}) ⇒ Types::UpdateAppImageConfigResponse
Updates the properties of an AppImageConfig.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 26206 def update_app_image_config(params = {}, = {}) req = build_request(:update_app_image_config, params) req.send_request() end |
#update_artifact(params = {}) ⇒ Types::UpdateArtifactResponse
Updates an artifact.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 26248 def update_artifact(params = {}, = {}) req = build_request(:update_artifact, params) req.send_request() end |
#update_cluster(params = {}) ⇒ Types::UpdateClusterResponse
Updates a SageMaker HyperPod cluster.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 26315 def update_cluster(params = {}, = {}) req = build_request(:update_cluster, params) req.send_request() end |
#update_cluster_scheduler_config(params = {}) ⇒ Types::UpdateClusterSchedulerConfigResponse
Update the cluster policy configuration.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 26365 def update_cluster_scheduler_config(params = {}, = {}) req = build_request(:update_cluster_scheduler_config, params) req.send_request() end |
#update_cluster_software(params = {}) ⇒ Types::UpdateClusterSoftwareResponse
Updates the platform software of a SageMaker HyperPod cluster for security patching. To learn how to use this API, see Update the SageMaker HyperPod platform software of a cluster.
The UpgradeClusterSoftware
API call may impact your SageMaker
HyperPod cluster uptime and availability. Plan accordingly to mitigate
potential disruptions to your workloads.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 26404 def update_cluster_software(params = {}, = {}) req = build_request(:update_cluster_software, params) req.send_request() end |
#update_code_repository(params = {}) ⇒ Types::UpdateCodeRepositoryOutput
Updates the specified Git repository with the specified values.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 26444 def update_code_repository(params = {}, = {}) req = build_request(:update_code_repository, params) req.send_request() end |
#update_compute_quota(params = {}) ⇒ Types::UpdateComputeQuotaResponse
Update the compute allocation definition.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 26514 def update_compute_quota(params = {}, = {}) req = build_request(:update_compute_quota, params) req.send_request() end |
#update_context(params = {}) ⇒ Types::UpdateContextResponse
Updates a context.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 26556 def update_context(params = {}, = {}) req = build_request(:update_context, params) req.send_request() end |
#update_device_fleet(params = {}) ⇒ Struct
Updates a fleet of devices.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 26604 def update_device_fleet(params = {}, = {}) req = build_request(:update_device_fleet, params) req.send_request() end |
#update_devices(params = {}) ⇒ Struct
Updates one or more devices in a fleet.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 26636 def update_devices(params = {}, = {}) req = build_request(:update_devices, params) req.send_request() end |
#update_domain(params = {}) ⇒ Types::UpdateDomainResponse
Updates the default settings for new user profiles in the domain.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 27031 def update_domain(params = {}, = {}) req = build_request(:update_domain, params) req.send_request() end |
#update_endpoint(params = {}) ⇒ Types::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.
When SageMaker receives the request, it sets the endpoint status to
Updating
. After updating the endpoint, it sets the status to
InService
. To check the status of an endpoint, use the
DescribeEndpoint API.
EndpointConfig
in use by an endpoint that is
live or while the UpdateEndpoint
or CreateEndpoint
operations are
being performed on the endpoint. To update an endpoint, you must
create a new EndpointConfig
.
If you delete the EndpointConfig
of an endpoint that is active or
being created or updated you may lose visibility into the instance
type the endpoint is using. The endpoint must be deleted in order to
stop incurring charges.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 27169 def update_endpoint(params = {}, = {}) req = build_request(:update_endpoint, params) req.send_request() end |
#update_endpoint_weights_and_capacities(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 27220 def update_endpoint_weights_and_capacities(params = {}, = {}) req = build_request(:update_endpoint_weights_and_capacities, params) req.send_request() end |
#update_experiment(params = {}) ⇒ Types::UpdateExperimentResponse
Adds, updates, or removes the description of an experiment. Updates the display name of an experiment.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 27259 def update_experiment(params = {}, = {}) req = build_request(:update_experiment, params) req.send_request() end |
#update_feature_group(params = {}) ⇒ Types::UpdateFeatureGroupResponse
Updates the feature group by either adding features or updating the
online store configuration. Use one of the following request
parameters at a time while using the UpdateFeatureGroup
API.
You can add features for your feature group using the
FeatureAdditions
request parameter. Features cannot be removed from
a feature group.
You can update the online store configuration by using the
OnlineStoreConfig
request parameter. If a TtlDuration
is
specified, the default TtlDuration
applies for all records added to
the feature group after the feature group is updated. If a record
level TtlDuration
exists from using the PutRecord
API, the record
level TtlDuration
applies to that record instead of the default
TtlDuration
. To remove the default TtlDuration
from an existing
feature group, use the UpdateFeatureGroup
API and set the
TtlDuration
Unit
and Value
to null
.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 27342 def update_feature_group(params = {}, = {}) req = build_request(:update_feature_group, params) req.send_request() end |
#update_feature_metadata(params = {}) ⇒ Struct
Updates the description and parameters of the feature group.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 27388 def (params = {}, = {}) req = build_request(:update_feature_metadata, params) req.send_request() end |
#update_hub(params = {}) ⇒ Types::UpdateHubResponse
Update a hub.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 27428 def update_hub(params = {}, = {}) req = build_request(:update_hub, params) req.send_request() end |
#update_hub_content(params = {}) ⇒ Types::UpdateHubContentResponse
Updates SageMaker hub content (either a Model
or Notebook
resource).
You can update the metadata that describes the resource. In addition to the required request fields, specify at least one of the following fields to update:
HubContentDescription
HubContentDisplayName
HubContentMarkdown
HubContentSearchKeywords
SupportStatus
For more information about hubs, see Private curated hubs for foundation model access control in JumpStart.
ModelReference
resource in your hub, use the
UpdateHubContentResource
API instead.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 27523 def update_hub_content(params = {}, = {}) req = build_request(:update_hub_content, params) req.send_request() end |
#update_hub_content_reference(params = {}) ⇒ Types::UpdateHubContentReferenceResponse
Updates the contents of a SageMaker hub for a ModelReference
resource. A ModelReference
allows you to access public SageMaker
JumpStart models from within your private hub.
When using this API, you can update the MinVersion
field for
additional flexibility in the model version. You shouldn't update any
additional fields when using this API, because the metadata in your
private hub should match the public JumpStart model's metadata.
Model
or Notebook
resource in your hub,
use the UpdateHubContent
API instead.
For more information about adding model references to your hub, see Add models to a private hub.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 27590 def update_hub_content_reference(params = {}, = {}) req = build_request(:update_hub_content_reference, params) req.send_request() end |
#update_image(params = {}) ⇒ Types::UpdateImageResponse
Updates the properties of a SageMaker AI image. To change the image's tags, use the AddTags and DeleteTags APIs.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 27642 def update_image(params = {}, = {}) req = build_request(:update_image, params) req.send_request() end |
#update_image_version(params = {}) ⇒ Types::UpdateImageVersionResponse
Updates the properties of a SageMaker AI image version.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 27739 def update_image_version(params = {}, = {}) req = build_request(:update_image_version, params) req.send_request() end |
#update_inference_component(params = {}) ⇒ Types::UpdateInferenceComponentOutput
Updates an inference component.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 27825 def update_inference_component(params = {}, = {}) req = build_request(:update_inference_component, params) req.send_request() end |
#update_inference_component_runtime_config(params = {}) ⇒ Types::UpdateInferenceComponentRuntimeConfigOutput
Runtime settings for a model that is deployed with an inference component.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 27861 def update_inference_component_runtime_config(params = {}, = {}) req = build_request(:update_inference_component_runtime_config, params) req.send_request() end |
#update_inference_experiment(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 27955 def update_inference_experiment(params = {}, = {}) req = build_request(:update_inference_experiment, params) req.send_request() end |
#update_mlflow_tracking_server(params = {}) ⇒ Types::UpdateMlflowTrackingServerResponse
Updates properties of an existing MLflow Tracking Server.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 28006 def update_mlflow_tracking_server(params = {}, = {}) req = build_request(:update_mlflow_tracking_server, params) req.send_request() end |
#update_model_card(params = {}) ⇒ Types::UpdateModelCardResponse
Update an Amazon SageMaker Model Card.
You cannot update both model card content and model card status in a single call.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 28064 def update_model_card(params = {}, = {}) req = build_request(:update_model_card, params) req.send_request() end |
#update_model_package(params = {}) ⇒ Types::UpdateModelPackageOutput
Updates a versioned model.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 28269 def update_model_package(params = {}, = {}) req = build_request(:update_model_package, params) req.send_request() end |
#update_monitoring_alert(params = {}) ⇒ Types::UpdateMonitoringAlertResponse
Update the parameters of a model monitor alert.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 28313 def update_monitoring_alert(params = {}, = {}) req = build_request(:update_monitoring_alert, params) req.send_request() end |
#update_monitoring_schedule(params = {}) ⇒ Types::UpdateMonitoringScheduleResponse
Updates a previously created schedule.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 28448 def update_monitoring_schedule(params = {}, = {}) req = build_request(:update_monitoring_schedule, params) req.send_request() end |
#update_notebook_instance(params = {}) ⇒ Struct
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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 28599 def update_notebook_instance(params = {}, = {}) req = build_request(:update_notebook_instance, params) req.send_request() end |
#update_notebook_instance_lifecycle_config(params = {}) ⇒ Struct
Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 28645 def update_notebook_instance_lifecycle_config(params = {}, = {}) req = build_request(:update_notebook_instance_lifecycle_config, params) req.send_request() end |
#update_partner_app(params = {}) ⇒ Types::UpdatePartnerAppResponse
Updates all of the SageMaker Partner AI Apps in an account.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 28717 def update_partner_app(params = {}, = {}) req = build_request(:update_partner_app, params) req.send_request() end |
#update_pipeline(params = {}) ⇒ Types::UpdatePipelineResponse
Updates a pipeline.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 28778 def update_pipeline(params = {}, = {}) req = build_request(:update_pipeline, params) req.send_request() end |
#update_pipeline_execution(params = {}) ⇒ Types::UpdatePipelineExecutionResponse
Updates a pipeline execution.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 28821 def update_pipeline_execution(params = {}, = {}) req = build_request(:update_pipeline_execution, params) req.send_request() end |
#update_project(params = {}) ⇒ Types::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.
ServiceCatalogProvisioningUpdateDetails
of a project that is active
or being created, or updated, you may lose resources already created
by the project.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 28902 def update_project(params = {}, = {}) req = build_request(:update_project, params) req.send_request() end |
#update_space(params = {}) ⇒ Types::UpdateSpaceResponse
Updates the settings of a space.
SpaceSettings
.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 29028 def update_space(params = {}, = {}) req = build_request(:update_space, params) req.send_request() end |
#update_training_job(params = {}) ⇒ Types::UpdateTrainingJobResponse
Update a model training job to request a new Debugger profiling configuration or to change warm pool retention length.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 29109 def update_training_job(params = {}, = {}) req = build_request(:update_training_job, params) req.send_request() end |
#update_trial(params = {}) ⇒ Types::UpdateTrialResponse
Updates the display name of a trial.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 29142 def update_trial(params = {}, = {}) req = build_request(:update_trial, params) req.send_request() end |
#update_trial_component(params = {}) ⇒ Types::UpdateTrialComponentResponse
Updates one or more properties of a trial component.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 29239 def update_trial_component(params = {}, = {}) req = build_request(:update_trial_component, params) req.send_request() end |
#update_user_profile(params = {}) ⇒ Types::UpdateUserProfileResponse
Updates a user profile.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 29475 def update_user_profile(params = {}, = {}) req = build_request(:update_user_profile, params) req.send_request() end |
#update_workforce(params = {}) ⇒ Types::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.
The worker portal is now supported in VPC and public internet.
Use SourceIpConfig
to restrict worker access to tasks to a specific
range of IP addresses. You specify allowed IP addresses by creating a
list of up to ten CIDRs. By default, a workforce isn't
restricted to specific IP addresses. If you specify a range of IP
addresses, workers who attempt to access tasks using any IP address
outside the specified range are denied and get a Not Found
error
message on the worker portal.
To restrict access to all the workers in public internet, add the
SourceIpConfig
CIDR value as "10.0.0.0/16".
Amazon SageMaker does not support Source Ip restriction for worker portals in VPC.
Use OidcConfig
to update the configuration of a workforce created
using your own OIDC IdP.
You can only update your OIDC IdP configuration when there are no work teams associated with your workforce. You can delete work teams using the DeleteWorkteam operation.
After restricting access to a range of IP addresses or updating your OIDC IdP configuration with this operation, you can view details about your update workforce using the DescribeWorkforce operation.
This operation only applies to private workforces.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 29611 def update_workforce(params = {}, = {}) req = build_request(:update_workforce, params) req.send_request() end |
#update_workteam(params = {}) ⇒ Types::UpdateWorkteamResponse
Updates an existing work team with new member definitions or description.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 29725 def update_workteam(params = {}, = {}) req = build_request(:update_workteam, params) req.send_request() end |
#wait_until(waiter_name, params = {}, options = {}) {|w.waiter| ... } ⇒ Boolean
Polls an API operation until a resource enters a desired state.
Basic Usage
A waiter will call an API operation until:
- It is successful
- It enters a terminal state
- It makes the maximum number of attempts
In between attempts, the waiter will sleep.
# polls in a loop, sleeping between attempts
client.wait_until(waiter_name, params)
Configuration
You can configure the maximum number of polling attempts, and the delay (in seconds) between each polling attempt. You can pass configuration as the final arguments hash.
# poll for ~25 seconds
client.wait_until(waiter_name, params, {
max_attempts: 5,
delay: 5,
})
Callbacks
You can be notified before each polling attempt and before each
delay. If you throw :success
or :failure
from these callbacks,
it will terminate the waiter.
started_at = Time.now
client.wait_until(waiter_name, params, {
# disable max attempts
max_attempts: nil,
# poll for 1 hour, instead of a number of attempts
before_wait: -> (attempts, response) do
throw :failure if Time.now - started_at > 3600
end
})
Handling Errors
When a waiter is unsuccessful, it will raise an error. All of the failure errors extend from Waiters::Errors::WaiterFailed.
begin
client.wait_until(...)
rescue Aws::Waiters::Errors::WaiterFailed
# resource did not enter the desired state in time
end
Valid Waiters
The following table lists the valid waiter names, the operations they call,
and the default :delay
and :max_attempts
values.
waiter_name | params | :delay | :max_attempts |
---|---|---|---|
endpoint_deleted | #describe_endpoint | 30 | 60 |
endpoint_in_service | #describe_endpoint | 30 | 120 |
image_created | #describe_image | 60 | 60 |
image_deleted | #describe_image | 60 | 60 |
image_updated | #describe_image | 60 | 60 |
image_version_created | #describe_image_version | 60 | 60 |
image_version_deleted | #describe_image_version | 60 | 60 |
notebook_instance_deleted | #describe_notebook_instance | 30 | 60 |
notebook_instance_in_service | #describe_notebook_instance | 30 | 60 |
notebook_instance_stopped | #describe_notebook_instance | 30 | 60 |
processing_job_completed_or_stopped | #describe_processing_job | 60 | 60 |
training_job_completed_or_stopped | #describe_training_job | 120 | 180 |
transform_job_completed_or_stopped | #describe_transform_job | 60 | 60 |
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 29852 def wait_until(waiter_name, params = {}, = {}) w = waiter(waiter_name, ) yield(w.waiter) if block_given? # deprecated w.wait(params) end |