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_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 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_code_repository(params = {}) ⇒ Types::CreateCodeRepositoryOutput
Creates a Git repository as a resource in your SageMaker account.
-
#create_compilation_job(params = {}) ⇒ Types::CreateCompilationJobResponse
Starts a model compilation job.
-
#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_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 image.
-
#create_image_version(params = {}) ⇒ Types::CreateImageVersionResponse
Creates a version of the SageMaker image specified by
ImageName
. -
#create_inference_component(params = {}) ⇒ Types::CreateInferenceComponentOutput
Creates an inference component, which is a SageMaker 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_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 Processing Jobs to monitor the data captured for an Amazon SageMaker Endpoint.
-
#create_notebook_instance(params = {}) ⇒ Types::CreateNotebookInstanceOutput
Creates an SageMaker notebook instance.
-
#create_notebook_instance_lifecycle_config(params = {}) ⇒ Types::CreateNotebookInstanceLifecycleConfigOutput
Creates a lifecycle configuration that you can associate with a notebook instance.
-
#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_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 Studio Lifecycle Configuration.
-
#create_training_job(params = {}) ⇒ Types::CreateTrainingJobResponse
Starts a model training job.
-
#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_code_repository(params = {}) ⇒ Struct
Deletes the specified Git repository from your account.
-
#delete_compilation_job(params = {}) ⇒ Struct
Deletes the specified compilation job.
-
#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_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 image and all versions of the image.
-
#delete_image_version(params = {}) ⇒ Struct
Deletes a version of a SageMaker image.
-
#delete_inference_component(params = {}) ⇒ Struct
Deletes an inference component.
-
#delete_inference_experiment(params = {}) ⇒ Types::DeleteInferenceExperimentResponse
Deletes an inference experiment.
-
#delete_model(params = {}) ⇒ Struct
Deletes a model.
-
#delete_model_bias_job_definition(params = {}) ⇒ Struct
Deletes an Amazon SageMaker 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 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 notebook instance.
-
#delete_notebook_instance_lifecycle_config(params = {}) ⇒ Struct
Deletes a notebook instance lifecycle configuration.
-
#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 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 an instance (also called a node interchangeably) of a SageMaker HyperPod cluster.
-
#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_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
Describe 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 image.
-
#describe_image_version(params = {}) ⇒ Types::DescribeImageVersionResponse
Describes a version of a SageMaker 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_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_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 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_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_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_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_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 notebook instances in the requester's account in an Amazon Web Services Region.
-
#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 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_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.
-
#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_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_monitoring_schedule(params = {}) ⇒ Struct
Stops a previously started monitoring schedule.
-
#stop_notebook_instance(params = {}) ⇒ Struct
Terminates the ML compute instance.
-
#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_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_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_image(params = {}) ⇒ Types::UpdateImageResponse
Updates the properties of a SageMaker image.
-
#update_image_version(params = {}) ⇒ Types::UpdateImageVersionResponse
Updates the properties of a SageMaker 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_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_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.
420 421 422 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 420 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.
482 483 484 485 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 482 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.
565 566 567 568 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 565 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.
605 606 607 608 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 605 def associate_trial_component(params = {}, = {}) req = build_request(:associate_trial_component, params) req.send_request() end |
#batch_describe_model_package(params = {}) ⇒ Types::BatchDescribeModelPackageOutput
This action batch describes a list of versioned model packages
671 672 673 674 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 671 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.
752 753 754 755 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 752 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.
1037 1038 1039 1040 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1037 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 upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.
1116 1117 1118 1119 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1116 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 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.
1215 1216 1217 1218 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1215 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.
1291 1292 1293 1294 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1291 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.
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.
1470 1471 1472 1473 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1470 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.
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.
1748 1749 1750 1751 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1748 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.
1832 1833 1834 1835 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1832 def create_cluster(params = {}, = {}) req = build_request(:create_cluster, params) req.send_request() end |
#create_code_repository(params = {}) ⇒ Types::CreateCodeRepositoryOutput
Creates a Git repository as a resource in your SageMaker account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your SageMaker account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with.
The repository can be hosted either in Amazon Web Services CodeCommit or in any other Git repository.
1900 1901 1902 1903 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1900 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 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 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 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.
2062 2063 2064 2065 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2062 def create_compilation_job(params = {}, = {}) req = build_request(:create_compilation_job, 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.
2129 2130 2131 2132 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2129 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 Model Monitor.
2294 2295 2296 2297 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2294 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.
2353 2354 2355 2356 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2353 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 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, 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 Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker 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 Studio app successfully.
For more information, see Connect Amazon SageMaker Studio Notebooks to Resources in a VPC.
2760 2761 2762 2763 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2760 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.
2829 2830 2831 2832 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2829 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.
2868 2869 2870 2871 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2868 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.
2935 2936 2937 2938 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2935 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.
3126 3127 3128 3129 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3126 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.
3450 3451 3452 3453 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3450 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.
3543 3544 3545 3546 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3543 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
.
3768 3769 3770 3771 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3768 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.
3859 3860 3861 3862 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3859 def create_flow_definition(params = {}, = {}) req = build_request(:create_flow_definition, params) req.send_request() end |
#create_hub(params = {}) ⇒ Types::CreateHubResponse
Create a hub.
3918 3919 3920 3921 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3918 def create_hub(params = {}, = {}) req = build_request(:create_hub, 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.
3965 3966 3967 3968 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3965 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.
4462 4463 4464 4465 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4462 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 image. A SageMaker 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 image.
4520 4521 4522 4523 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4520 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 image specified by ImageName
. The
version represents the Amazon ECR container image specified by
BaseImage
.
4625 4626 4627 4628 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4625 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 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.
4719 4720 4721 4722 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4719 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.
4918 4919 4920 4921 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4918 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.
5081 5082 5083 5084 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5081 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.
5390 5391 5392 5393 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5390 def create_labeling_job(params = {}, = {}) req = build_request(:create_labeling_job, 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.
5574 5575 5576 5577 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5574 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.
5731 5732 5733 5734 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5731 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.
5807 5808 5809 5810 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5807 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.
5851 5852 5853 5854 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5851 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.
6006 6007 6008 6009 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6006 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.
6450 6451 6452 6453 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6450 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.
6498 6499 6500 6501 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6498 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 Model Monitor.
6664 6665 6666 6667 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6664 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 Processing Jobs to monitor the data captured for an Amazon SageMaker Endpoint.
6812 6813 6814 6815 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6812 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 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 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 also provides a set of example notebooks. Each notebook demonstrates how to use SageMaker with a specific algorithm or with a machine learning framework.
After receiving the request, SageMaker does the following:
Creates a network interface in the SageMaker VPC.
(Option) If you specified
SubnetId
, SageMaker 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 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 VPC. If you specified
SubnetId
of your VPC, SageMaker 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 returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it.
After SageMaker 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 endpoints, and validate hosted models.
For more information, see How It Works.
7038 7039 7040 7041 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 7038 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.
7107 7108 7109 7110 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 7107 def create_notebook_instance_lifecycle_config(params = {}, = {}) req = build_request(:create_notebook_instance_lifecycle_config, params) req.send_request() end |
#create_pipeline(params = {}) ⇒ Types::CreatePipelineResponse
Creates a pipeline using a JSON pipeline definition.
7192 7193 7194 7195 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 7192 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 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.
7291 7292 7293 7294 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 7291 def create_presigned_domain_url(params = {}, = {}) req = build_request(:create_presigned_domain_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 console, when you choose Open
next to a notebook instance, SageMaker opens a new tab showing the
Jupyter server home page from the notebook instance. The console uses
this API to get the URL and show the page.
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.
7353 7354 7355 7356 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 7353 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.
7537 7538 7539 7540 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 7537 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.
7611 7612 7613 7614 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 7611 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.
7744 7745 7746 7747 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 7744 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 Studio Lifecycle Configuration.
7793 7794 7795 7796 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 7793 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.
8274 8275 8276 8277 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 8274 def create_training_job(params = {}, = {}) req = build_request(:create_training_job, 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.
8507 8508 8509 8510 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 8507 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.
8589 8590 8591 8592 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 8589 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.
8715 8716 8717 8718 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 8715 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.
8946 8947 8948 8949 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 8946 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).
9062 9063 9064 9065 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9062 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.
9179 9180 9181 9182 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9179 def create_workteam(params = {}, = {}) req = build_request(:create_workteam, params) req.send_request() end |
#delete_action(params = {}) ⇒ Types::DeleteActionResponse
Deletes an action.
9207 9208 9209 9210 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9207 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.
9229 9230 9231 9232 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9229 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.
9269 9270 9271 9272 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9269 def delete_app(params = {}, = {}) req = build_request(:delete_app, params) req.send_request() end |
#delete_app_image_config(params = {}) ⇒ Struct
Deletes an AppImageConfig.
9291 9292 9293 9294 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9291 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.
9332 9333 9334 9335 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9332 def delete_artifact(params = {}, = {}) req = build_request(:delete_artifact, params) req.send_request() end |
#delete_association(params = {}) ⇒ Types::DeleteAssociationResponse
Deletes an association.
9366 9367 9368 9369 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9366 def delete_association(params = {}, = {}) req = build_request(:delete_association, params) req.send_request() end |
#delete_cluster(params = {}) ⇒ Types::DeleteClusterResponse
Delete a SageMaker HyperPod cluster.
9395 9396 9397 9398 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9395 def delete_cluster(params = {}, = {}) req = build_request(:delete_cluster, params) req.send_request() end |
#delete_code_repository(params = {}) ⇒ Struct
Deletes the specified Git repository from your account.
9417 9418 9419 9420 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9417 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. 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
.
9448 9449 9450 9451 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9448 def delete_compilation_job(params = {}, = {}) req = build_request(:delete_compilation_job, params) req.send_request() end |
#delete_context(params = {}) ⇒ Types::DeleteContextResponse
Deletes an context.
9476 9477 9478 9479 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9476 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.
9498 9499 9500 9501 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9498 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.
9520 9521 9522 9523 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9520 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.
9553 9554 9555 9556 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9553 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.
9576 9577 9578 9579 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9576 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.
9604 9605 9606 9607 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9604 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.
9641 9642 9643 9644 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9641 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.
9672 9673 9674 9675 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9672 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.
9706 9707 9708 9709 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9706 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
.
9739 9740 9741 9742 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9739 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.
9761 9762 9763 9764 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9761 def delete_flow_definition(params = {}, = {}) req = build_request(:delete_flow_definition, params) req.send_request() end |
#delete_hub(params = {}) ⇒ Struct
Delete a hub.
9787 9788 9789 9790 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9787 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.
9825 9826 9827 9828 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9825 def delete_hub_content(params = {}, = {}) req = build_request(:delete_hub_content, 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
.
9857 9858 9859 9860 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9857 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.
9883 9884 9885 9886 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9883 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 image and all versions of the image. The container images aren't deleted.
9906 9907 9908 9909 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9906 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 image. The container image the version represents isn't deleted.
9937 9938 9939 9940 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9937 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.
9959 9960 9961 9962 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9959 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.
9993 9994 9995 9996 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 9993 def delete_inference_experiment(params = {}, = {}) req = build_request(:delete_inference_experiment, 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.
10018 10019 10020 10021 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10018 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 model bias job definition.
10040 10041 10042 10043 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10040 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.
10062 10063 10064 10065 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10062 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 model explainability job definition.
10084 10085 10086 10087 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10084 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.
10114 10115 10116 10117 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10114 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.
10136 10137 10138 10139 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10136 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.
10158 10159 10160 10161 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10158 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.
10180 10181 10182 10183 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10180 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.
10204 10205 10206 10207 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10204 def delete_monitoring_schedule(params = {}, = {}) req = build_request(:delete_monitoring_schedule, params) req.send_request() end |
#delete_notebook_instance(params = {}) ⇒ Struct
Deletes an SageMaker 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 removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.
10232 10233 10234 10235 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10232 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.
10254 10255 10256 10257 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10254 def delete_notebook_instance_lifecycle_config(params = {}, = {}) req = build_request(:delete_notebook_instance_lifecycle_config, 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.
10294 10295 10296 10297 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10294 def delete_pipeline(params = {}, = {}) req = build_request(:delete_pipeline, params) req.send_request() end |
#delete_project(params = {}) ⇒ Struct
Delete the specified project.
10316 10317 10318 10319 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10316 def delete_project(params = {}, = {}) req = build_request(:delete_project, params) req.send_request() end |
#delete_space(params = {}) ⇒ Struct
Used to delete a space.
10342 10343 10344 10345 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10342 def delete_space(params = {}, = {}) req = build_request(:delete_space, params) req.send_request() end |
#delete_studio_lifecycle_config(params = {}) ⇒ Struct
Deletes the Amazon SageMaker 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.
10368 10369 10370 10371 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10368 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.
10409 10410 10411 10412 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10409 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.
10443 10444 10445 10446 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10443 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.
10478 10479 10480 10481 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10478 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.
10506 10507 10508 10509 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10506 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 recieve a ResourceInUse
error.
10543 10544 10545 10546 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10543 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.
10571 10572 10573 10574 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10571 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.
10598 10599 10600 10601 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10598 def deregister_devices(params = {}, = {}) req = build_request(:deregister_devices, params) req.send_request() end |
#describe_action(params = {}) ⇒ Types::DescribeActionResponse
Describes an action.
10666 10667 10668 10669 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10666 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.
10839 10840 10841 10842 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10839 def describe_algorithm(params = {}, = {}) req = build_request(:describe_algorithm, params) req.send_request() end |
#describe_app(params = {}) ⇒ Types::DescribeAppResponse
Describes the app.
10910 10911 10912 10913 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10910 def describe_app(params = {}, = {}) req = build_request(:describe_app, params) req.send_request() end |
#describe_app_image_config(params = {}) ⇒ Types::DescribeAppImageConfigResponse
Describes an AppImageConfig.
10971 10972 10973 10974 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 10971 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.
11036 11037 11038 11039 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11036 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
.
11177 11178 11179 11180 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11177 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.
11351 11352 11353 11354 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11351 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.
11403 11404 11405 11406 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11403 def describe_cluster(params = {}, = {}) req = build_request(:describe_cluster, params) req.send_request() end |
#describe_cluster_node(params = {}) ⇒ Types::DescribeClusterNodeResponse
Retrieves information of an instance (also called a node interchangeably) of a SageMaker HyperPod cluster.
11445 11446 11447 11448 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11445 def describe_cluster_node(params = {}, = {}) req = build_request(:describe_cluster_node, params) req.send_request() end |
#describe_code_repository(params = {}) ⇒ Types::DescribeCodeRepositoryOutput
Gets details about the specified Git repository.
11483 11484 11485 11486 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11483 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.
11568 11569 11570 11571 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11568 def describe_compilation_job(params = {}, = {}) req = build_request(:describe_compilation_job, params) req.send_request() end |
#describe_context(params = {}) ⇒ Types::DescribeContextResponse
Describes a context.
11629 11630 11631 11632 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11629 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.
11722 11723 11724 11725 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11722 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.
11782 11783 11784 11785 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11782 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.
11827 11828 11829 11830 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 11827 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.
12040 12041 12042 12043 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 12040 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.
12112 12113 12114 12115 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 12112 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.
12174 12175 12176 12177 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 12174 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
12381 12382 12383 12384 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 12381 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.
12511 12512 12513 12514 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 12511 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.
12566 12567 12568 12569 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 12566 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.
12655 12656 12657 12658 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 12655 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.
12704 12705 12706 12707 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 12704 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.
12762 12763 12764 12765 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 12762 def describe_flow_definition(params = {}, = {}) req = build_request(:describe_flow_definition, params) req.send_request() end |
#describe_hub(params = {}) ⇒ Types::DescribeHubResponse
Describe a hub.
12813 12814 12815 12816 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 12813 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.
12890 12891 12892 12893 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 12890 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).
12929 12930 12931 12932 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 12929 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.
13224 13225 13226 13227 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 13224 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 image.
The following waiters are defined for this operation (see #wait_until for detailed usage):
- image_created
- image_deleted
- image_updated
13275 13276 13277 13278 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 13275 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 image.
The following waiters are defined for this operation (see #wait_until for detailed usage):
- image_version_created
- image_version_deleted
13348 13349 13350 13351 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 13348 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.
13409 13410 13411 13412 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 13409 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.
13485 13486 13487 13488 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 13485 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.
13614 13615 13616 13617 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 13614 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.
13710 13711 13712 13713 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 13710 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.
13768 13769 13770 13771 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 13768 def describe_lineage_group(params = {}, = {}) req = build_request(:describe_lineage_group, params) req.send_request() end |
#describe_model(params = {}) ⇒ Types::DescribeModelOutput
Describes a model that you created using the CreateModel
API.
13851 13852 13853 13854 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 13851 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.
13941 13942 13943 13944 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 13941 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.
14005 14006 14007 14008 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 14005 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.
14052 14053 14054 14055 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 14052 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.
14141 14142 14143 14144 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 14141 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.
To create models in SageMaker, buyers can subscribe to model packages listed on Amazon Web Services Marketplace.
14380 14381 14382 14383 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 14380 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.
14423 14424 14425 14426 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 14423 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.
14518 14519 14520 14521 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 14518 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.
14631 14632 14633 14634 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 14631 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
14711 14712 14713 14714 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 14711 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.
14758 14759 14760 14761 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 14758 def describe_notebook_instance_lifecycle_config(params = {}, = {}) req = build_request(:describe_notebook_instance_lifecycle_config, params) req.send_request() end |
#describe_pipeline(params = {}) ⇒ Types::DescribePipelineResponse
Describes the details of a pipeline.
14820 14821 14822 14823 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 14820 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.
14850 14851 14852 14853 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 14850 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.
14915 14916 14917 14918 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 14915 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
15040 15041 15042 15043 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 15040 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.
15104 15105 15106 15107 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 15104 def describe_project(params = {}, = {}) req = build_request(:describe_project, params) req.send_request() end |
#describe_space(params = {}) ⇒ Types::DescribeSpaceResponse
Describes the space.
15195 15196 15197 15198 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 15195 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 Studio Lifecycle Configuration.
15234 15235 15236 15237 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 15234 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.
15269 15270 15271 15272 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 15269 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
15489 15490 15491 15492 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 15489 def describe_training_job(params = {}, = {}) req = build_request(:describe_training_job, 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
15580 15581 15582 15583 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 15580 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.
15640 15641 15642 15643 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 15640 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.
15734 15735 15736 15737 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 15734 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
.
15874 15875 15876 15877 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 15874 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.
15936 15937 15938 15939 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 15936 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 create date, the last updated date, membership information, and the work team's Amazon Resource Name (ARN).
15983 15984 15985 15986 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 15983 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.
15997 15998 15999 16000 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 15997 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
.
16045 16046 16047 16048 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16045 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.
16059 16060 16061 16062 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16059 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.
16113 16114 16115 16116 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16113 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.
16143 16144 16145 16146 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16143 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..
16178 16179 16180 16181 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16178 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.
16198 16199 16200 16201 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16198 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.
16282 16283 16284 16285 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16282 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
.
16322 16323 16324 16325 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16322 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.
16403 16404 16405 16406 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16403 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.
16477 16478 16479 16480 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16477 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.
16544 16545 16546 16547 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16544 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.
16596 16597 16598 16599 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16596 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.
16702 16703 16704 16705 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16702 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.
16780 16781 16782 16783 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16780 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.
16855 16856 16857 16858 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16855 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.
16950 16951 16952 16953 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 16950 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.
17029 17030 17031 17032 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 17029 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.
17121 17122 17123 17124 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 17121 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.
17224 17225 17226 17227 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 17224 def list_cluster_nodes(params = {}, = {}) req = build_request(:list_cluster_nodes, 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.
17319 17320 17321 17322 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 17319 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.
17397 17398 17399 17400 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 17397 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.
17494 17495 17496 17497 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 17494 def list_compilation_jobs(params = {}, = {}) req = build_request(:list_compilation_jobs, 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.
17568 17569 17570 17571 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 17568 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.
17641 17642 17643 17644 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 17641 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.
17711 17712 17713 17714 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 17711 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.
17772 17773 17774 17775 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 17772 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.
17822 17823 17824 17825 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 17822 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.
17901 17902 17903 17904 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 17901 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.
17982 17983 17984 17985 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 17982 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.
18046 18047 18048 18049 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18046 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.
18127 18128 18129 18130 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18127 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.
18194 18195 18196 18197 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18194 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.
18267 18268 18269 18270 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18267 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.
18326 18327 18328 18329 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18326 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.
18415 18416 18417 18418 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18415 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.
18498 18499 18500 18501 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18498 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.
18575 18576 18577 18578 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18575 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.
18633 18634 18635 18636 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18633 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.
18731 18732 18733 18734 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18731 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.
18811 18812 18813 18814 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18811 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.
18893 18894 18895 18896 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18893 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.
18991 18992 18993 18994 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 18991 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.
19088 19089 19090 19091 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 19088 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.
19173 19174 19175 19176 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 19173 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.
19272 19273 19274 19275 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 19272 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.
19368 19369 19370 19371 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 19368 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.
19443 19444 19445 19446 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 19443 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.
19511 19512 19513 19514 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 19511 def list_lineage_groups(params = {}, = {}) req = build_request(:list_lineage_groups, 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.
19581 19582 19583 19584 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 19581 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.
19662 19663 19664 19665 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 19662 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.
19734 19735 19736 19737 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 19734 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.
19802 19803 19804 19805 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 19802 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.
19874 19875 19876 19877 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 19874 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.
19933 19934 19935 19936 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 19933 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.
19999 20000 20001 20002 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 19999 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.
20091 20092 20093 20094 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 20091 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.
20166 20167 20168 20169 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 20166 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.
20230 20231 20232 20233 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 20230 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.
20303 20304 20305 20306 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 20303 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.
20352 20353 20354 20355 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 20352 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.
20456 20457 20458 20459 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 20456 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.
20556 20557 20558 20559 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 20556 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.
20637 20638 20639 20640 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 20637 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 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.
20751 20752 20753 20754 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 20751 def list_notebook_instances(params = {}, = {}) req = build_request(:list_notebook_instances, 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.
20848 20849 20850 20851 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 20848 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.
20914 20915 20916 20917 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 20914 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.
20959 20960 20961 20962 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 20959 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.
21027 21028 21029 21030 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 21027 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.
21110 21111 21112 21113 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 21110 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.
21177 21178 21179 21180 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 21177 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.
21241 21242 21243 21244 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 21241 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.
21311 21312 21313 21314 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 21311 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.
21372 21373 21374 21375 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 21372 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 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.
21458 21459 21460 21461 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 21458 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.
21509 21510 21511 21512 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 21509 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.
21554 21555 21556 21557 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 21554 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.
21666 21667 21668 21669 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 21666 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.
21745 21746 21747 21748 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 21745 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_transform_jobs(params = {}) ⇒ Types::ListTransformJobsResponse
Lists transform jobs.
The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.
21828 21829 21830 21831 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 21828 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.
21936 21937 21938 21939 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 21936 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.
22013 22014 22015 22016 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22013 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.
22078 22079 22080 22081 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22078 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.
22153 22154 22155 22156 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22153 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.
22225 22226 22227 22228 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22225 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..
22264 22265 22266 22267 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22264 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.
22371 22372 22373 22374 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22371 def query_lineage(params = {}, = {}) req = build_request(:query_lineage, params) req.send_request() end |
#register_devices(params = {}) ⇒ Struct
Register devices.
22412 22413 22414 22415 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22412 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.
22470 22471 22472 22473 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22470 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.
22514 22515 22516 22517 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22514 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.
22637 22638 22639 22640 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22637 def search(params = {}, = {}) req = build_request(:search, 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).
22681 22682 22683 22684 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22681 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).
22730 22731 22732 22733 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22730 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.
22756 22757 22758 22759 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22756 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.
22784 22785 22786 22787 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22784 def start_inference_experiment(params = {}, = {}) req = build_request(:start_inference_experiment, params) req.send_request() end |
#start_monitoring_schedule(params = {}) ⇒ Struct
Starts a previously stopped monitoring schedule.
scheduled
.
22811 22812 22813 22814 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22811 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 sets the notebook instance status to
InService
. A notebook instance's status must be InService
before
you can connect to your Jupyter notebook.
22837 22838 22839 22840 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22837 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.
22909 22910 22911 22912 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22909 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.
22931 22932 22933 22934 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22931 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 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
changes the CompilationJobStatus
of the job to Stopping
. After
Amazon SageMaker stops the job, it sets the CompilationJobStatus
to
Stopped
.
22962 22963 22964 22965 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22962 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.
22988 22989 22990 22991 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 22988 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.
23010 23011 23012 23013 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23010 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.
23039 23040 23041 23042 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23039 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.
23112 23113 23114 23115 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23112 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.
23134 23135 23136 23137 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23134 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.
23158 23159 23160 23161 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23158 def stop_labeling_job(params = {}, = {}) req = build_request(:stop_labeling_job, params) req.send_request() end |
#stop_monitoring_schedule(params = {}) ⇒ Struct
Stops a previously started monitoring schedule.
23180 23181 23182 23183 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23180 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 disconnects the ML storage volume from it. SageMaker
preserves the ML storage volume. SageMaker 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.
23211 23212 23213 23214 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23211 def stop_notebook_instance(params = {}, = {}) req = build_request(:stop_notebook_instance, 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
.
23275 23276 23277 23278 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23275 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.
23297 23298 23299 23300 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23297 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
.
23326 23327 23328 23329 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23326 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.
23354 23355 23356 23357 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23354 def stop_transform_job(params = {}, = {}) req = build_request(:stop_transform_job, params) req.send_request() end |
#update_action(params = {}) ⇒ Types::UpdateActionResponse
Updates an action.
23400 23401 23402 23403 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23400 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.
23478 23479 23480 23481 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23478 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.
23520 23521 23522 23523 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23520 def update_artifact(params = {}, = {}) req = build_request(:update_artifact, params) req.send_request() end |
#update_cluster(params = {}) ⇒ Types::UpdateClusterResponse
Updates a SageMaker HyperPod cluster.
23564 23565 23566 23567 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23564 def update_cluster(params = {}, = {}) req = build_request(:update_cluster, 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.
23599 23600 23601 23602 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23599 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.
23639 23640 23641 23642 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23639 def update_code_repository(params = {}, = {}) req = build_request(:update_code_repository, params) req.send_request() end |
#update_context(params = {}) ⇒ Types::UpdateContextResponse
Updates a context.
23681 23682 23683 23684 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23681 def update_context(params = {}, = {}) req = build_request(:update_context, params) req.send_request() end |
#update_device_fleet(params = {}) ⇒ Struct
Updates a fleet of devices.
23729 23730 23731 23732 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23729 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.
23761 23762 23763 23764 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 23761 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.
24087 24088 24089 24090 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 24087 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.
24225 24226 24227 24228 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 24225 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.
24276 24277 24278 24279 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 24276 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.
24315 24316 24317 24318 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 24315 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
.
24398 24399 24400 24401 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 24398 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.
24444 24445 24446 24447 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 24444 def (params = {}, = {}) req = build_request(:update_feature_metadata, params) req.send_request() end |
#update_hub(params = {}) ⇒ Types::UpdateHubResponse
Update a hub.
24488 24489 24490 24491 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 24488 def update_hub(params = {}, = {}) req = build_request(:update_hub, params) req.send_request() end |
#update_image(params = {}) ⇒ Types::UpdateImageResponse
Updates the properties of a SageMaker image. To change the image's tags, use the AddTags and DeleteTags APIs.
24540 24541 24542 24543 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 24540 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 image version.
24637 24638 24639 24640 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 24637 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.
24696 24697 24698 24699 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 24696 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.
24732 24733 24734 24735 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 24732 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.
24826 24827 24828 24829 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 24826 def update_inference_experiment(params = {}, = {}) req = build_request(:update_inference_experiment, 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.
24884 24885 24886 24887 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 24884 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.
25038 25039 25040 25041 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25038 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.
25082 25083 25084 25085 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25082 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.
25217 25218 25219 25220 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25217 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.
25368 25369 25370 25371 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25368 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.
25414 25415 25416 25417 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25414 def update_notebook_instance_lifecycle_config(params = {}, = {}) req = build_request(:update_notebook_instance_lifecycle_config, params) req.send_request() end |
#update_pipeline(params = {}) ⇒ Types::UpdatePipelineResponse
Updates a pipeline.
25475 25476 25477 25478 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25475 def update_pipeline(params = {}, = {}) req = build_request(:update_pipeline, params) req.send_request() end |
#update_pipeline_execution(params = {}) ⇒ Types::UpdatePipelineExecutionResponse
Updates a pipeline execution.
25518 25519 25520 25521 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25518 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.
25599 25600 25601 25602 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25599 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.
25708 25709 25710 25711 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25708 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.
25789 25790 25791 25792 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25789 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.
25822 25823 25824 25825 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25822 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.
25919 25920 25921 25922 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 25919 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.
26113 26114 26115 26116 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 26113 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.
26242 26243 26244 26245 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 26242 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.
26356 26357 26358 26359 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 26356 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 |
26478 26479 26480 26481 26482 |
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 26478 def wait_until(waiter_name, params = {}, = {}) w = waiter(waiter_name, ) yield(w.waiter) if block_given? # deprecated w.wait(params) end |