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 Amazon SageMaker resource.
-
#associate_trial_component(params = {}) ⇒ Types::AssociateTrialComponentResponse
Associates a trial component with a trial.
-
#create_action(params = {}) ⇒ Types::CreateActionResponse
Creates an action.
-
#create_algorithm(params = {}) ⇒ Types::CreateAlgorithmOutput
Create a machine learning algorithm that you can use in Amazon SageMaker and list in the AWS 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.
-
#create_code_repository(params = {}) ⇒ Types::CreateCodeRepositoryOutput
Creates a Git repository as a resource in your Amazon 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
used by Amazon SageMaker Studio. -
#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 Amazon SageMaker hosting services uses to deploy models.
-
#create_experiment(params = {}) ⇒ Types::CreateExperimentResponse
Creates an SageMaker experiment.
-
#create_feature_group(params = {}) ⇒ Types::CreateFeatureGroupResponse
Create a new
FeatureGroup
. -
#create_flow_definition(params = {}) ⇒ Types::CreateFlowDefinitionResponse
Creates a flow definition.
-
#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_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 Amazon SageMaker.
-
#create_model_bias_job_definition(params = {}) ⇒ Types::CreateModelBiasJobDefinitionResponse
Creates the definition for a model bias 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 Amazon SageMaker models or list on AWS 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 Endoint.
-
#create_notebook_instance(params = {}) ⇒ Types::CreateNotebookInstanceOutput
Creates an Amazon 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_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 Amazon 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_code_repository(params = {}) ⇒ Struct
Deletes the specified Git repository from your account.
-
#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_endpoint(params = {}) ⇒ Struct
Deletes an endpoint.
-
#delete_endpoint_config(params = {}) ⇒ Struct
Deletes an endpoint configuration.
-
#delete_experiment(params = {}) ⇒ Types::DeleteExperimentResponse
Deletes an Amazon 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_human_task_ui(params = {}) ⇒ Struct
Use this operation to delete a human task user interface (worker task template).
-
#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_model(params = {}) ⇒ Struct
Deletes a model.
-
#delete_model_bias_job_definition(params = {}) ⇒ Struct
Deletes an Amazon SageMaker model bias job definition.
-
#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 Amazon 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 in-progress executions.
-
#delete_project(params = {}) ⇒ Struct
Delete the specified project.
-
#delete_tags(params = {}) ⇒ Struct
Deletes the specified tags from an Amazon 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 Amazon SageMaker AutoML job.
-
#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_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_flow_definition(params = {}) ⇒ Types::DescribeFlowDefinitionResponse
Returns information about the specified flow definition.
-
#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
Gets a description of a hyperparameter tuning job.
-
#describe_image(params = {}) ⇒ Types::DescribeImageResponse
Describes a SageMaker image.
-
#describe_image_version(params = {}) ⇒ Types::DescribeImageVersionResponse
Describes a version of a SageMaker image.
-
#describe_labeling_job(params = {}) ⇒ Types::DescribeLabelingJobResponse
Gets information about a labeling job.
-
#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_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 Amazon SageMaker models or list them on AWS 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_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_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_search_suggestions(params = {}) ⇒ Types::GetSearchSuggestionsResponse
An auto-complete API for the search functionality in the Amazon SageMaker console.
-
#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_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_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_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_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 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_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_model_bias_job_definitions(params = {}) ⇒ Types::ListModelBiasJobDefinitionsResponse
Lists model bias jobs definitions that satisfy various filters.
-
#list_model_explainability_job_definitions(params = {}) ⇒ Types::ListModelExplainabilityJobDefinitionsResponse
Lists model explainability job definitions that satisfy various filters.
-
#list_model_package_groups(params = {}) ⇒ Types::ListModelPackageGroupsOutput
Gets a list of the model groups in your AWS 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_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 API.
-
#list_notebook_instances(params = {}) ⇒ Types::ListNotebookInstancesOutput
Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS 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 AWS account.
-
#list_subscribed_workteams(params = {}) ⇒ Types::ListSubscribedWorkteamsResponse
Gets a list of the work teams that you are subscribed to in the AWS Marketplace.
-
#list_tags(params = {}) ⇒ Types::ListTagsOutput
Returns the tags for the specified Amazon 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 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 AWS 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.
-
#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.
-
#search(params = {}) ⇒ Types::SearchResponse
Finds Amazon SageMaker resources that match a search query.
-
#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 the termination of a running job.
-
#stop_compilation_job(params = {}) ⇒ Struct
Stops a model compilation job.
-
#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_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 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_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 new
EndpointConfig
specified in the request, switches to using newly created endpoint, and then deletes resources provisioned for the endpoint using the previousEndpointConfig
(there is no availability loss). -
#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_image(params = {}) ⇒ Types::UpdateImageResponse
Updates the properties of a SageMaker image.
-
#update_model_package(params = {}) ⇒ Types::UpdateModelPackageOutput
Updates a versioned model.
-
#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 API.
-
#update_pipeline(params = {}) ⇒ Types::UpdatePipelineResponse
Updates a pipeline.
-
#update_pipeline_execution(params = {}) ⇒ Types::UpdatePipelineExecutionResponse
Updates a pipeline execution.
-
#update_training_job(params = {}) ⇒ Types::UpdateTrainingJobResponse
Update a model training job to request a new Debugger profiling configuration.
-
#update_trial(params = {}) ⇒ Types::UpdateTrialResponse
Updates the display name of a trial.
-
#update_trial_component(params = {}) ⇒ Types::UpdateTrialComponentResponse
Updates one or more properties of a trial component.
-
#update_user_profile(params = {}) ⇒ Types::UpdateUserProfileResponse
Updates a user profile.
-
#update_workforce(params = {}) ⇒ Types::UpdateWorkforceResponse
Use this operation to update your workforce.
-
#update_workteam(params = {}) ⇒ Types::UpdateWorkteamResponse
Updates an existing work team with new member definitions or description.
Instance Method Summary collapse
-
#initialize(options) ⇒ Client
constructor
A new instance of Client.
-
#wait_until(waiter_name, params = {}, options = {}) {|w.waiter| ... } ⇒ Boolean
Polls an API operation until a resource enters a desired state.
Methods included from ClientStubs
#api_requests, #stub_data, #stub_responses
Methods inherited from Seahorse::Client::Base
add_plugin, api, clear_plugins, define, new, #operation_names, plugins, remove_plugin, set_api, set_plugins
Methods included from Seahorse::Client::HandlerBuilder
#handle, #handle_request, #handle_response
Constructor Details
#initialize(options) ⇒ Client
Returns a new instance of Client.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 334 def initialize(*args) super end |
Instance Method Details
#add_association(params = {}) ⇒ Types::AddAssociationResponse
Creates an association between the source and the destination. A source can be associated with multiple destinations, and a destination can be associated with multiple sources. An association is a lineage tracking entity. For more information, see Amazon SageMaker ML Lineage Tracking.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 396 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 Amazon SageMaker resource. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints.
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 AWS Tagging Strategies.
Tags
parameter of
CreateHyperParameterTuningJob
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 464 def (params = {}, = {}) req = build_request(:add_tags, params) req.send_request() end |
#associate_trial_component(params = {}) ⇒ Types::AssociateTrialComponentResponse
Associates a trial component with a trial. A trial component can be associated with multiple trials. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 500 def associate_trial_component(params = {}, = {}) req = build_request(:associate_trial_component, params) req.send_request() end |
#create_action(params = {}) ⇒ Types::CreateActionResponse
Creates an action. An action is a lineage tracking entity that represents an action or activity. For example, a model deployment or an HPO job. Generally, an action involves at least one input or output artifact. For more information, see Amazon SageMaker ML Lineage Tracking.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 581 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 Amazon SageMaker and list in the AWS Marketplace.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 825 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. Supported apps
are JupyterServer
and KernelGateway
. This operation is
automatically invoked by Amazon SageMaker Studio upon access to the
associated Domain, and when new kernel configurations are selected by
the user. A user may have multiple Apps active simultaneously.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 889 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 (EFS) storage volume on the image, and a list of the kernels in the image.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 945 def create_app_image_config(params = {}, = {}) req = build_request(:create_app_image_config, params) req.send_request() end |
#create_artifact(params = {}) ⇒ Types::CreateArtifactResponse
Creates an artifact. An artifact is a lineage tracking entity that represents a URI addressable object or data. Some examples are the S3 URI of a dataset and the ECR registry path of an image. For more information, see Amazon SageMaker ML Lineage Tracking.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1021 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.
Find the best performing model after you run an Autopilot job by calling .
For information about how to use Autopilot, see Automate Model Development with Amazon SageMaker Autopilot.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1145 def create_auto_ml_job(params = {}, = {}) req = build_request(:create_auto_ml_job, params) req.send_request() end |
#create_code_repository(params = {}) ⇒ Types::CreateCodeRepositoryOutput
Creates a Git repository as a resource in your Amazon SageMaker account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your Amazon SageMaker account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with.
The repository can be hosted either in AWS CodeCommit or in any other Git repository.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1212 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 AWS 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1343 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1410 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1551 def create_data_quality_job_definition(params = {}, = {}) req = build_request(:create_data_quality_job_definition, params) req.send_request() end |
#create_device_fleet(params = {}) ⇒ Struct
Creates a device fleet.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1599 def create_device_fleet(params = {}, = {}) req = build_request(:create_device_fleet, params) req.send_request() end |
#create_domain(params = {}) ⇒ Types::CreateDomainResponse
Creates a Domain
used by Amazon SageMaker Studio. A domain consists
of an associated Amazon Elastic File System (EFS) volume, a list of
authorized users, and a variety of security, application, policy, and
Amazon Virtual Private Cloud (VPC) configurations. An AWS account is
limited to one domain per region. Users within a domain can share
notebook files and other artifacts with each other.
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 AWS Key Management Service (AWS KMS) to encrypt the EFS volume attached to the domain with an AWS managed customer master key (CMK) by default. For more control, you can specify a customer managed CMK. For more information, see Protect Data at Rest Using Encryption.
VPC configuration
All SageMaker Studio traffic between the domain and the EFS volume is
through the specified VPC and subnets. For other Studio traffic, you
can specify the AppNetworkAccessType
parameter.
AppNetworkAccessType
corresponds to the network access type that you
choose when you onboard to Studio. 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 Studio 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 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.
For more information, see Connect SageMaker Studio Notebooks to Resources in a VPC.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1769 def create_domain(params = {}, = {}) req = build_request(:create_domain, params) req.send_request() end |
#create_edge_packaging_job(params = {}) ⇒ Struct
Starts a SageMaker Edge Manager model packaging job. Edge Manager will use the model artifacts from the Amazon Simple Storage Service bucket that you specify. After the model has been packaged, Amazon SageMaker saves the resulting artifacts to an S3 bucket that you specify.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1834 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. Amazon 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 Amazon SageMaker hosting services.
For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
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 AWS Region in your AWS account.
When it receives the request, Amazon 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 Amazon SageMaker receives the request, it sets the endpoint
status to Creating
. After it creates the endpoint, it sets the
status to InService
. Amazon 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, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provided. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.
Option 1: For a full Amazon 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 Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1973 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 Amazon SageMaker hosting
services uses to deploy models. In the configuration, you identify one
or more models, created using the CreateModel
API, to deploy and the
resources that you want Amazon SageMaker to provision. Then you call
the CreateEndpoint API.
In the request, you define a ProductionVariant
, for each model that
you want to deploy. Each ProductionVariant
parameter also describes
the resources that you want Amazon SageMaker to provision. This
includes the number and type of ML compute instances to deploy.
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. Amazon
SageMaker distributes two-thirds of the traffic to Model A, and
one-third to model B.
For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
Eventually Consistent Reads
,
the response might not reflect the results of a recently completed
write operation. The response might include some stale data. If the
dependent entities are not yet in DynamoDB, this causes a validation
error. If you repeat your read request after a short time, the
response should return the latest data. So retry logic is recommended
to handle these possible issues. We also recommend that customers call
DescribeEndpointConfig before calling CreateEndpoint to minimize the
potential impact of a DynamoDB eventually consistent read.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2143 def create_endpoint_config(params = {}, = {}) req = build_request(:create_endpoint_config, params) req.send_request() end |
#create_experiment(params = {}) ⇒ Types::CreateExperimentResponse
Creates an SageMaker experiment. An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called trial components, that produce a machine learning model.
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 Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to experiments, trials, trial components and then use the Search API to search for the tags.
To add a description to an experiment, specify the optional
Description
parameter. To add a description later, or to change the
description, call the UpdateExperiment API.
To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To create a trial call the CreateTrial API.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2217 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
AWS service quotas to see the FeatureGroup
s quota for your AWS
account.
You must include at least one of OnlineStoreConfig
and
OfflineStoreConfig
to create a FeatureGroup
.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2385 def create_feature_group(params = {}, = {}) req = build_request(:create_feature_group, params) req.send_request() end |
#create_flow_definition(params = {}) ⇒ Types::CreateFlowDefinitionResponse
Creates a flow definition.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2476 def create_flow_definition(params = {}, = {}) req = build_request(:create_flow_definition, params) req.send_request() end |
#create_human_task_ui(params = {}) ⇒ Types::CreateHumanTaskUiResponse
Defines the settings you will use for the human review workflow user interface. Reviewers will see a three-panel interface with an instruction area, the item to review, and an input area.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2523 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2863 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 Container Registry (ECR). For more information, see Bring your own SageMaker image.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2921 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 Container Registry (ECR) container image
specified by BaseImage
.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2968 def create_image_version(params = {}, = {}) req = build_request(:create_image_version, 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 AWS Marketplace. Vendors provide expertise in specific areas.
The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information.
You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling.
The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data.
The output can be used as the manifest file for another labeling job or as training data for your machine learning models.
You can use this operation to create a static labeling job or a
streaming labeling job. A static labeling job stops if all data
objects in the input manifest file identified in ManifestS3Uri
have
been labeled. A streaming labeling job runs perpetually until it is
manually stopped, or remains idle for 10 days. You can send new data
objects to an active (InProgress
) streaming labeling job in real
time. To learn how to create a static labeling job, see Create a
Labeling Job (API) in the Amazon SageMaker Developer Guide. To
learn how to create a streaming labeling job, see Create a Streaming
Labeling Job.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3263 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 Amazon SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions.
Use this API to create a model if you want to use Amazon 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. Amazon SageMaker then deploys all of the
containers that you defined for the model in the hosting environment.
For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
To run a batch transform using your model, you start a job with the
CreateTransformJob
API. Amazon SageMaker uses your model and your
dataset to get inferences which are then saved to a specified S3
location.
In the CreateModel
request, you must define a container with the
PrimaryContainer
parameter.
In the request, you also provide an IAM role that Amazon 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 AWS resources, you grant necessary permissions via this role.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3435 def create_model(params = {}, = {}) req = build_request(:create_model, params) req.send_request() end |
#create_model_bias_job_definition(params = {}) ⇒ Types::CreateModelBiasJobDefinitionResponse
Creates the definition for a model bias job.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3568 def create_model_bias_job_definition(params = {}, = {}) req = build_request(:create_model_bias_job_definition, params) req.send_request() end |
#create_model_explainability_job_definition(params = {}) ⇒ Types::CreateModelExplainabilityJobDefinitionResponse
Creates the definition for a model explainability job.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3698 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 Amazon SageMaker models or list on AWS Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon SageMaker.
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
AWS Marketplace, provide a value for SourceAlgorithmSpecification
.
Versioned - a model that is part of a model group in the model registry.
Unversioned - a model package that is not part of a model group.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3932 def create_model_package(params = {}, = {}) req = build_request(:create_model_package, params) req.send_request() end |
#create_model_package_group(params = {}) ⇒ Types::CreateModelPackageGroupOutput
Creates a model group. A model group contains a group of model versions.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3980 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4122 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 Endoint.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4244 def create_monitoring_schedule(params = {}, = {}) req = build_request(:create_monitoring_schedule, params) req.send_request() end |
#create_notebook_instance(params = {}) ⇒ Types::CreateNotebookInstanceOutput
Creates an Amazon 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. Amazon 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.
Amazon SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use Amazon SageMaker with a specific algorithm or with a machine learning framework.
After receiving the request, Amazon SageMaker does the following:
Creates a network interface in the Amazon SageMaker VPC.
(Option) If you specified
SubnetId
, Amazon 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, Amazon 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 Amazon SageMaker VPC. If you specified
SubnetId
of your VPC, Amazon 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, Amazon SageMaker returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it.
After Amazon 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 Amazon SageMaker endpoints, and validate hosted models.
For more information, see How It Works.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4461 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 CloudWatch Logs for notebook instance lifecycle configurations in
log group /aws/sagemaker/NotebookInstances
in log stream
[notebook-instance-name]/[LifecycleConfigHook]
.
Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4530 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4593 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 Amazon SageMaker Studio, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System (EFS) volume. This operation can only be called when the authentication mode equals IAM.
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 AWS console sign-in page.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4647 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 Amazon SageMaker console, when you choose
Open
next to a notebook instance, Amazon SageMaker opens a new tab
showing the Jupyter server home page from the notebook instance. The
console uses this API to get the URL and show the page.
The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance.
You can restrict access to this API and to the URL that it returns to
a list of IP addresses that you specify. Use the NotIpAddress
condition operator and the aws:SourceIP
condition context key to
specify the list of IP addresses that you want to have access to the
notebook instance. For more information, see Limit Access to a
Notebook Instance by IP Address.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4708 def create_presigned_notebook_instance_url(params = {}, = {}) req = build_request(:create_presigned_notebook_instance_url, params) req.send_request() end |
#create_processing_job(params = {}) ⇒ Types::CreateProcessingJobResponse
Creates a processing job.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4885 def create_processing_job(params = {}, = {}) req = build_request(:create_processing_job, params) req.send_request() end |
#create_project(params = {}) ⇒ Types::CreateProjectOutput
Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4955 def create_project(params = {}, = {}) req = build_request(:create_project, params) req.send_request() end |
#create_training_job(params = {}) ⇒ Types::CreateTrainingJobResponse
Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location 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 in a machine learning service other than Amazon 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 Amazon SageMaker, see Algorithms.InputDataConfig
- Describes the training dataset and the Amazon S3, EFS, or FSx location where it is stored.OutputDataConfig
- Identifies the Amazon S3 bucket where you want Amazon 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 Amazon SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that Amazon 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 you are willing to wait for a managed spot training job to complete.Environment
- The environment variables to set in the Docker container.
For more information about Amazon SageMaker, see How It Works.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5360 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 AWS Region in an AWS account.ModelName
- Identifies the model to use.ModelName
must be the name of an existing Amazon SageMaker model in the same AWS Region and AWS account. For information on creating a model, see CreateModel.TransformInput
- Describes the dataset to be transformed and the Amazon S3 location where it is stored.TransformOutput
- Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.TransformResources
- Identifies the ML compute instances for the transform job.
For more information about how batch transformation works, see Batch Transform.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5570 def create_transform_job(params = {}, = {}) req = build_request(:create_transform_job, params) req.send_request() end |
#create_trial(params = {}) ⇒ Types::CreateTrialResponse
Creates an Amazon SageMaker trial. A trial is a set of steps called trial components that produce a machine learning model. A trial is part of a single Amazon SageMaker experiment.
When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to a trial and then use the Search API to search for the tags.
To get a list of all your trials, call the ListTrials API. To view a trial's properties, call the DescribeTrial API. To create a trial component, call the CreateTrialComponent API.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5641 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 Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS 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.
CreateTrialComponent
can only be invoked from within an Amazon
SageMaker managed environment. This includes Amazon SageMaker training
jobs, processing jobs, transform jobs, and Amazon SageMaker notebooks.
A call to CreateTrialComponent
from outside one of these
environments results in an error.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5767 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 Amazon SageMaker Studio. If an administrator invites a person by email or imports them from SSO, a user profile is automatically created. A user profile is the primary holder of settings for an individual user and has a reference to the user's private Amazon Elastic File System (EFS) home directory.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5870 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 AWS Region that you specify. You can only create one workforce in each AWS Region per AWS account.
If you want to create a new workforce in an AWS Region where a
workforce already exists, use the API operation to delete the existing
workforce and then use CreateWorkforce
to create a new workforce.
To create a private workforce using Amazon Cognito, you must specify a
Cognito user pool in CognitoConfig
. You can also create an Amazon
Cognito workforce using the Amazon SageMaker console. For more
information, see Create a Private Workforce (Amazon Cognito).
To create a private workforce using your own OIDC Identity Provider
(IdP), specify your IdP configuration in OidcConfig
. Your OIDC IdP
must support groups because groups are used by Ground Truth and
Amazon A2I to create work teams. For more information, see Create a
Private Workforce (OIDC IdP).
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5976 def create_workforce(params = {}, = {}) req = build_request(:create_workforce, params) req.send_request() end |
#create_workteam(params = {}) ⇒ Types::CreateWorkteamResponse
Creates a new work team for labeling your data. A work team is defined by one or more Amazon Cognito user pools. You must first create the user pools before you can create a work team.
You cannot create more than 25 work teams in an account and region.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6078 def create_workteam(params = {}, = {}) req = build_request(:create_workteam, params) req.send_request() end |
#delete_action(params = {}) ⇒ Types::DeleteActionResponse
Deletes an action.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6106 def delete_action(params = {}, = {}) req = build_request(:delete_action, params) req.send_request() end |
#delete_algorithm(params = {}) ⇒ Struct
Removes the specified algorithm from your account.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6128 def delete_algorithm(params = {}, = {}) req = build_request(:delete_algorithm, params) req.send_request() end |
#delete_app(params = {}) ⇒ Struct
Used to stop and delete an app.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6162 def delete_app(params = {}, = {}) req = build_request(:delete_app, params) req.send_request() end |
#delete_app_image_config(params = {}) ⇒ Struct
Deletes an AppImageConfig.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6184 def delete_app_image_config(params = {}, = {}) req = build_request(:delete_app_image_config, params) req.send_request() end |
#delete_artifact(params = {}) ⇒ Types::DeleteArtifactResponse
Deletes an artifact. Either ArtifactArn
or Source
must be
specified.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6225 def delete_artifact(params = {}, = {}) req = build_request(:delete_artifact, params) req.send_request() end |
#delete_association(params = {}) ⇒ Types::DeleteAssociationResponse
Deletes an association.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6259 def delete_association(params = {}, = {}) req = build_request(:delete_association, params) req.send_request() end |
#delete_code_repository(params = {}) ⇒ Struct
Deletes the specified Git repository from your account.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6281 def delete_code_repository(params = {}, = {}) req = build_request(:delete_code_repository, params) req.send_request() end |
#delete_context(params = {}) ⇒ Types::DeleteContextResponse
Deletes an context.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6309 def delete_context(params = {}, = {}) req = build_request(:delete_context, params) req.send_request() end |
#delete_data_quality_job_definition(params = {}) ⇒ Struct
Deletes a data quality monitoring job definition.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6331 def delete_data_quality_job_definition(params = {}, = {}) req = build_request(:delete_data_quality_job_definition, params) req.send_request() end |
#delete_device_fleet(params = {}) ⇒ Struct
Deletes a fleet.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6353 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 SSO. Use with caution. All of the members of the domain will lose access to their EFS volume, including data, notebooks, and other artifacts.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6386 def delete_domain(params = {}, = {}) req = build_request(:delete_domain, params) req.send_request() end |
#delete_endpoint(params = {}) ⇒ Struct
Deletes an endpoint. Amazon SageMaker frees up all of the resources that were deployed when the endpoint was created.
Amazon SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the RevokeGrant API call.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6417 def delete_endpoint(params = {}, = {}) req = build_request(:delete_endpoint, params) req.send_request() end |
#delete_endpoint_config(params = {}) ⇒ Struct
Deletes an endpoint configuration. The DeleteEndpointConfig
API
deletes only the specified configuration. It does not delete endpoints
created using the configuration.
You must not delete an EndpointConfig
in use by an endpoint that is
live or while the UpdateEndpoint
or CreateEndpoint
operations are
being performed on the endpoint. If you delete the EndpointConfig
of
an endpoint that is active or being created or updated you may lose
visibility into the instance type the endpoint is using. The endpoint
must be deleted in order to stop incurring charges.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6448 def delete_endpoint_config(params = {}, = {}) req = build_request(:delete_endpoint_config, params) req.send_request() end |
#delete_experiment(params = {}) ⇒ Types::DeleteExperimentResponse
Deletes an Amazon SageMaker experiment. All trials associated with the experiment must be deleted first. Use the ListTrials API to get a list of the trials associated with the experiment.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6478 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 AWS Glue
database and tables that are automatically created for your
OfflineStore
are not deleted.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6507 def delete_feature_group(params = {}, = {}) req = build_request(:delete_feature_group, params) req.send_request() end |
#delete_flow_definition(params = {}) ⇒ Struct
Deletes the specified flow definition.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6529 def delete_flow_definition(params = {}, = {}) req = build_request(:delete_flow_definition, 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 . When you delete a worker task template, it no
longer appears when you call ListHumanTaskUis
.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6557 def delete_human_task_ui(params = {}, = {}) req = build_request(:delete_human_task_ui, 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6580 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6607 def delete_image_version(params = {}, = {}) req = build_request(:delete_image_version, params) req.send_request() end |
#delete_model(params = {}) ⇒ Struct
Deletes a model. The DeleteModel
API deletes only the model entry
that was created in Amazon SageMaker when you called the CreateModel
API. It does not delete model artifacts, inference code, or the IAM
role that you specified when creating the model.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6632 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6654 def delete_model_bias_job_definition(params = {}, = {}) req = build_request(:delete_model_bias_job_definition, params) req.send_request() end |
#delete_model_explainability_job_definition(params = {}) ⇒ Struct
Deletes an Amazon SageMaker model explainability job definition.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6676 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 Amazon SageMaker models or list on AWS Marketplace. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon SageMaker.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6703 def delete_model_package(params = {}, = {}) req = build_request(:delete_model_package, params) req.send_request() end |
#delete_model_package_group(params = {}) ⇒ Struct
Deletes the specified model group.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6725 def delete_model_package_group(params = {}, = {}) req = build_request(:delete_model_package_group, params) req.send_request() end |
#delete_model_package_group_policy(params = {}) ⇒ Struct
Deletes a model group resource policy.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6747 def delete_model_package_group_policy(params = {}, = {}) req = build_request(:delete_model_package_group_policy, params) req.send_request() end |
#delete_model_quality_job_definition(params = {}) ⇒ Struct
Deletes the secified model quality monitoring job definition.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6769 def delete_model_quality_job_definition(params = {}, = {}) req = build_request(:delete_model_quality_job_definition, params) req.send_request() end |
#delete_monitoring_schedule(params = {}) ⇒ Struct
Deletes a monitoring schedule. Also stops the schedule had not already been stopped. This does not delete the job execution history of the monitoring schedule.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6793 def delete_monitoring_schedule(params = {}, = {}) req = build_request(:delete_monitoring_schedule, params) req.send_request() end |
#delete_notebook_instance(params = {}) ⇒ Struct
Deletes an Amazon 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. Amazon SageMaker removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6821 def delete_notebook_instance(params = {}, = {}) req = build_request(:delete_notebook_instance, params) req.send_request() end |
#delete_notebook_instance_lifecycle_config(params = {}) ⇒ Struct
Deletes a notebook instance lifecycle configuration.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6843 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 in-progress executions.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 6880 def delete_pipeline(params = {}, = {}) req = build_request(:delete_pipeline, params) req.send_request() end |
#delete_project(params = {}) ⇒ Struct
Delete the specified project.