Class: Aws::SageMaker::Client
- Inherits:
-
Seahorse::Client::Base
- Object
- Seahorse::Client::Base
- Aws::SageMaker::Client
- Includes:
- ClientStubs
- Defined in:
- gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb
Overview
An API client for SageMaker. To construct a client, you need to configure a :region
and :credentials
.
client = Aws::SageMaker::Client.new(
region: region_name,
credentials: credentials,
# ...
)
For details on configuring region and credentials see the developer guide.
See #initialize for a full list of supported configuration options.
Instance Attribute Summary
Attributes inherited from Seahorse::Client::Base
API Operations collapse
-
#add_association(params = {}) ⇒ Types::AddAssociationResponse
Creates an association between the source and the destination.
-
#add_tags(params = {}) ⇒ Types::AddTagsOutput
Adds or overwrites one or more tags for the specified SageMaker resource.
-
#associate_trial_component(params = {}) ⇒ Types::AssociateTrialComponentResponse
Associates a trial component with a trial.
-
#batch_delete_cluster_nodes(params = {}) ⇒ Types::BatchDeleteClusterNodesResponse
Deletes specific nodes within a SageMaker HyperPod cluster.
-
#batch_describe_model_package(params = {}) ⇒ Types::BatchDescribeModelPackageOutput
This action batch describes a list of versioned model packages.
-
#create_action(params = {}) ⇒ Types::CreateActionResponse
Creates an action.
-
#create_algorithm(params = {}) ⇒ Types::CreateAlgorithmOutput
Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.
-
#create_app(params = {}) ⇒ Types::CreateAppResponse
Creates a running app for the specified UserProfile.
-
#create_app_image_config(params = {}) ⇒ Types::CreateAppImageConfigResponse
Creates a configuration for running a SageMaker image as a KernelGateway app.
-
#create_artifact(params = {}) ⇒ Types::CreateArtifactResponse
Creates an artifact.
-
#create_auto_ml_job(params = {}) ⇒ Types::CreateAutoMLJobResponse
Creates an Autopilot job also referred to as Autopilot experiment or AutoML job.
-
#create_auto_ml_job_v2(params = {}) ⇒ Types::CreateAutoMLJobV2Response
Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.
-
#create_cluster(params = {}) ⇒ Types::CreateClusterResponse
Creates a SageMaker HyperPod cluster.
-
#create_cluster_scheduler_config(params = {}) ⇒ Types::CreateClusterSchedulerConfigResponse
Create cluster policy configuration.
-
#create_code_repository(params = {}) ⇒ Types::CreateCodeRepositoryOutput
Creates a Git repository as a resource in your SageMaker account.
-
#create_compilation_job(params = {}) ⇒ Types::CreateCompilationJobResponse
Starts a model compilation job.
-
#create_compute_quota(params = {}) ⇒ Types::CreateComputeQuotaResponse
Create compute allocation definition.
-
#create_context(params = {}) ⇒ Types::CreateContextResponse
Creates a context.
-
#create_data_quality_job_definition(params = {}) ⇒ Types::CreateDataQualityJobDefinitionResponse
Creates a definition for a job that monitors data quality and drift.
-
#create_device_fleet(params = {}) ⇒ Struct
Creates a device fleet.
-
#create_domain(params = {}) ⇒ Types::CreateDomainResponse
Creates a
Domain
. -
#create_edge_deployment_plan(params = {}) ⇒ Types::CreateEdgeDeploymentPlanResponse
Creates an edge deployment plan, consisting of multiple stages.
-
#create_edge_deployment_stage(params = {}) ⇒ Struct
Creates a new stage in an existing edge deployment plan.
-
#create_edge_packaging_job(params = {}) ⇒ Struct
Starts a SageMaker Edge Manager model packaging job.
-
#create_endpoint(params = {}) ⇒ Types::CreateEndpointOutput
Creates an endpoint using the endpoint configuration specified in the request.
-
#create_endpoint_config(params = {}) ⇒ Types::CreateEndpointConfigOutput
Creates an endpoint configuration that SageMaker hosting services uses to deploy models.
-
#create_experiment(params = {}) ⇒ Types::CreateExperimentResponse
Creates a SageMaker experiment.
-
#create_feature_group(params = {}) ⇒ Types::CreateFeatureGroupResponse
Create a new
FeatureGroup
. -
#create_flow_definition(params = {}) ⇒ Types::CreateFlowDefinitionResponse
Creates a flow definition.
-
#create_hub(params = {}) ⇒ Types::CreateHubResponse
Create a hub.
-
#create_hub_content_reference(params = {}) ⇒ Types::CreateHubContentReferenceResponse
Create a hub content reference in order to add a model in the JumpStart public hub to a private hub.
-
#create_human_task_ui(params = {}) ⇒ Types::CreateHumanTaskUiResponse
Defines the settings you will use for the human review workflow user interface.
-
#create_hyper_parameter_tuning_job(params = {}) ⇒ Types::CreateHyperParameterTuningJobResponse
Starts a hyperparameter tuning job.
-
#create_image(params = {}) ⇒ Types::CreateImageResponse
Creates a custom SageMaker 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_mlflow_tracking_server(params = {}) ⇒ Types::CreateMlflowTrackingServerResponse
Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store.
-
#create_model(params = {}) ⇒ Types::CreateModelOutput
Creates a model in SageMaker.
-
#create_model_bias_job_definition(params = {}) ⇒ Types::CreateModelBiasJobDefinitionResponse
Creates the definition for a model bias job.
-
#create_model_card(params = {}) ⇒ Types::CreateModelCardResponse
Creates an Amazon SageMaker Model Card.
-
#create_model_card_export_job(params = {}) ⇒ Types::CreateModelCardExportJobResponse
Creates an Amazon SageMaker Model Card export job.
-
#create_model_explainability_job_definition(params = {}) ⇒ Types::CreateModelExplainabilityJobDefinitionResponse
Creates the definition for a model explainability job.
-
#create_model_package(params = {}) ⇒ Types::CreateModelPackageOutput
Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group.
-
#create_model_package_group(params = {}) ⇒ Types::CreateModelPackageGroupOutput
Creates a model group.
-
#create_model_quality_job_definition(params = {}) ⇒ Types::CreateModelQualityJobDefinitionResponse
Creates a definition for a job that monitors model quality and drift.
-
#create_monitoring_schedule(params = {}) ⇒ Types::CreateMonitoringScheduleResponse
Creates a schedule that regularly starts Amazon SageMaker 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_optimization_job(params = {}) ⇒ Types::CreateOptimizationJobResponse
Creates a job that optimizes a model for inference performance.
-
#create_partner_app(params = {}) ⇒ Types::CreatePartnerAppResponse
Creates an Amazon SageMaker Partner AI App.
-
#create_partner_app_presigned_url(params = {}) ⇒ Types::CreatePartnerAppPresignedUrlResponse
Creates a presigned URL to access an Amazon SageMaker Partner AI App.
-
#create_pipeline(params = {}) ⇒ Types::CreatePipelineResponse
Creates a pipeline using a JSON pipeline definition.
-
#create_presigned_domain_url(params = {}) ⇒ Types::CreatePresignedDomainUrlResponse
Creates a URL for a specified UserProfile in a Domain.
-
#create_presigned_mlflow_tracking_server_url(params = {}) ⇒ Types::CreatePresignedMlflowTrackingServerUrlResponse
Returns a presigned URL that you can use to connect to the MLflow UI attached to your tracking server.
-
#create_presigned_notebook_instance_url(params = {}) ⇒ Types::CreatePresignedNotebookInstanceUrlOutput
Returns a URL that you can use to connect to the Jupyter server from a notebook instance.
-
#create_processing_job(params = {}) ⇒ Types::CreateProcessingJobResponse
Creates a processing job.
-
#create_project(params = {}) ⇒ Types::CreateProjectOutput
Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model.
-
#create_space(params = {}) ⇒ Types::CreateSpaceResponse
Creates a private space or a space used for real time collaboration in a domain.
-
#create_studio_lifecycle_config(params = {}) ⇒ Types::CreateStudioLifecycleConfigResponse
Creates a new Amazon SageMaker Studio Lifecycle Configuration.
-
#create_training_job(params = {}) ⇒ Types::CreateTrainingJobResponse
Starts a model training job.
-
#create_training_plan(params = {}) ⇒ Types::CreateTrainingPlanResponse
Creates a new training plan in SageMaker to reserve compute capacity.
-
#create_transform_job(params = {}) ⇒ Types::CreateTransformJobResponse
Starts a transform job.
-
#create_trial(params = {}) ⇒ Types::CreateTrialResponse
Creates an SageMaker trial.
-
#create_trial_component(params = {}) ⇒ Types::CreateTrialComponentResponse
Creates a trial component, which is a stage of a machine learning trial.
-
#create_user_profile(params = {}) ⇒ Types::CreateUserProfileResponse
Creates a user profile.
-
#create_workforce(params = {}) ⇒ Types::CreateWorkforceResponse
Use this operation to create a workforce.
-
#create_workteam(params = {}) ⇒ Types::CreateWorkteamResponse
Creates a new work team for labeling your data.
-
#delete_action(params = {}) ⇒ Types::DeleteActionResponse
Deletes an action.
-
#delete_algorithm(params = {}) ⇒ Struct
Removes the specified algorithm from your account.
-
#delete_app(params = {}) ⇒ Struct
Used to stop and delete an app.
-
#delete_app_image_config(params = {}) ⇒ Struct
Deletes an AppImageConfig.
-
#delete_artifact(params = {}) ⇒ Types::DeleteArtifactResponse
Deletes an artifact.
-
#delete_association(params = {}) ⇒ Types::DeleteAssociationResponse
Deletes an association.
-
#delete_cluster(params = {}) ⇒ Types::DeleteClusterResponse
Delete a SageMaker HyperPod cluster.
-
#delete_cluster_scheduler_config(params = {}) ⇒ Struct
Deletes the cluster policy of the cluster.
-
#delete_code_repository(params = {}) ⇒ Struct
Deletes the specified Git repository from your account.
-
#delete_compilation_job(params = {}) ⇒ Struct
Deletes the specified compilation job.
-
#delete_compute_quota(params = {}) ⇒ Struct
Deletes the compute allocation from the cluster.
-
#delete_context(params = {}) ⇒ Types::DeleteContextResponse
Deletes an context.
-
#delete_data_quality_job_definition(params = {}) ⇒ Struct
Deletes a data quality monitoring job definition.
-
#delete_device_fleet(params = {}) ⇒ Struct
Deletes a fleet.
-
#delete_domain(params = {}) ⇒ Struct
Used to delete a domain.
-
#delete_edge_deployment_plan(params = {}) ⇒ Struct
Deletes an edge deployment plan if (and only if) all the stages in the plan are inactive or there are no stages in the plan.
-
#delete_edge_deployment_stage(params = {}) ⇒ Struct
Delete a stage in an edge deployment plan if (and only if) the stage is inactive.
-
#delete_endpoint(params = {}) ⇒ Struct
Deletes an endpoint.
-
#delete_endpoint_config(params = {}) ⇒ Struct
Deletes an endpoint configuration.
-
#delete_experiment(params = {}) ⇒ Types::DeleteExperimentResponse
Deletes an SageMaker experiment.
-
#delete_feature_group(params = {}) ⇒ Struct
Delete the
FeatureGroup
and any data that was written to theOnlineStore
of theFeatureGroup
. -
#delete_flow_definition(params = {}) ⇒ Struct
Deletes the specified flow definition.
-
#delete_hub(params = {}) ⇒ Struct
Delete a hub.
-
#delete_hub_content(params = {}) ⇒ Struct
Delete the contents of a hub.
-
#delete_hub_content_reference(params = {}) ⇒ Struct
Delete a hub content reference in order to remove a model from a private hub.
-
#delete_human_task_ui(params = {}) ⇒ Struct
Use this operation to delete a human task user interface (worker task template).
-
#delete_hyper_parameter_tuning_job(params = {}) ⇒ Struct
Deletes a hyperparameter tuning job.
-
#delete_image(params = {}) ⇒ Struct
Deletes a SageMaker 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_mlflow_tracking_server(params = {}) ⇒ Types::DeleteMlflowTrackingServerResponse
Deletes an MLflow Tracking Server.
-
#delete_model(params = {}) ⇒ Struct
Deletes a model.
-
#delete_model_bias_job_definition(params = {}) ⇒ Struct
Deletes an Amazon SageMaker 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_optimization_job(params = {}) ⇒ Struct
Deletes an optimization job.
-
#delete_partner_app(params = {}) ⇒ Types::DeletePartnerAppResponse
Deletes a SageMaker Partner AI App.
-
#delete_pipeline(params = {}) ⇒ Types::DeletePipelineResponse
Deletes a pipeline if there are no running instances of the pipeline.
-
#delete_project(params = {}) ⇒ Struct
Delete the specified project.
-
#delete_space(params = {}) ⇒ Struct
Used to delete a space.
-
#delete_studio_lifecycle_config(params = {}) ⇒ Struct
Deletes the Amazon SageMaker Studio Lifecycle Configuration.
-
#delete_tags(params = {}) ⇒ Struct
Deletes the specified tags from an SageMaker resource.
-
#delete_trial(params = {}) ⇒ Types::DeleteTrialResponse
Deletes the specified trial.
-
#delete_trial_component(params = {}) ⇒ Types::DeleteTrialComponentResponse
Deletes the specified trial component.
-
#delete_user_profile(params = {}) ⇒ Struct
Deletes a user profile.
-
#delete_workforce(params = {}) ⇒ Struct
Use this operation to delete a workforce.
-
#delete_workteam(params = {}) ⇒ Types::DeleteWorkteamResponse
Deletes an existing work team.
-
#deregister_devices(params = {}) ⇒ Struct
Deregisters the specified devices.
-
#describe_action(params = {}) ⇒ Types::DescribeActionResponse
Describes an action.
-
#describe_algorithm(params = {}) ⇒ Types::DescribeAlgorithmOutput
Returns a description of the specified algorithm that is in your account.
-
#describe_app(params = {}) ⇒ Types::DescribeAppResponse
Describes the app.
-
#describe_app_image_config(params = {}) ⇒ Types::DescribeAppImageConfigResponse
Describes an AppImageConfig.
-
#describe_artifact(params = {}) ⇒ Types::DescribeArtifactResponse
Describes an artifact.
-
#describe_auto_ml_job(params = {}) ⇒ Types::DescribeAutoMLJobResponse
Returns information about an AutoML job created by calling [CreateAutoMLJob][1].
-
#describe_auto_ml_job_v2(params = {}) ⇒ Types::DescribeAutoMLJobV2Response
Returns information about an AutoML job created by calling [CreateAutoMLJobV2][1] or [CreateAutoMLJob][2].
-
#describe_cluster(params = {}) ⇒ Types::DescribeClusterResponse
Retrieves information of a SageMaker HyperPod cluster.
-
#describe_cluster_node(params = {}) ⇒ Types::DescribeClusterNodeResponse
Retrieves information of a node (also called a instance interchangeably) of a SageMaker HyperPod cluster.
-
#describe_cluster_scheduler_config(params = {}) ⇒ Types::DescribeClusterSchedulerConfigResponse
Description of the cluster policy.
-
#describe_code_repository(params = {}) ⇒ Types::DescribeCodeRepositoryOutput
Gets details about the specified Git repository.
-
#describe_compilation_job(params = {}) ⇒ Types::DescribeCompilationJobResponse
Returns information about a model compilation job.
-
#describe_compute_quota(params = {}) ⇒ Types::DescribeComputeQuotaResponse
Description of the compute allocation definition.
-
#describe_context(params = {}) ⇒ Types::DescribeContextResponse
Describes a context.
-
#describe_data_quality_job_definition(params = {}) ⇒ Types::DescribeDataQualityJobDefinitionResponse
Gets the details of a data quality monitoring job definition.
-
#describe_device(params = {}) ⇒ Types::DescribeDeviceResponse
Describes the device.
-
#describe_device_fleet(params = {}) ⇒ Types::DescribeDeviceFleetResponse
A description of the fleet the device belongs to.
-
#describe_domain(params = {}) ⇒ Types::DescribeDomainResponse
The description of the domain.
-
#describe_edge_deployment_plan(params = {}) ⇒ Types::DescribeEdgeDeploymentPlanResponse
Describes an edge deployment plan with deployment status per stage.
-
#describe_edge_packaging_job(params = {}) ⇒ Types::DescribeEdgePackagingJobResponse
A description of edge packaging jobs.
-
#describe_endpoint(params = {}) ⇒ Types::DescribeEndpointOutput
Returns the description of an endpoint.
-
#describe_endpoint_config(params = {}) ⇒ Types::DescribeEndpointConfigOutput
Returns the description of an endpoint configuration created using the
CreateEndpointConfig
API. -
#describe_experiment(params = {}) ⇒ Types::DescribeExperimentResponse
Provides a list of an experiment's properties.
-
#describe_feature_group(params = {}) ⇒ Types::DescribeFeatureGroupResponse
Use this operation to describe a
FeatureGroup
. -
#describe_feature_metadata(params = {}) ⇒ Types::DescribeFeatureMetadataResponse
Shows the metadata for a feature within a feature group.
-
#describe_flow_definition(params = {}) ⇒ Types::DescribeFlowDefinitionResponse
Returns information about the specified flow definition.
-
#describe_hub(params = {}) ⇒ Types::DescribeHubResponse
Describes a hub.
-
#describe_hub_content(params = {}) ⇒ Types::DescribeHubContentResponse
Describe the content of a hub.
-
#describe_human_task_ui(params = {}) ⇒ Types::DescribeHumanTaskUiResponse
Returns information about the requested human task user interface (worker task template).
-
#describe_hyper_parameter_tuning_job(params = {}) ⇒ Types::DescribeHyperParameterTuningJobResponse
Returns a description of a hyperparameter tuning job, depending on the fields selected.
-
#describe_image(params = {}) ⇒ Types::DescribeImageResponse
Describes a SageMaker 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_mlflow_tracking_server(params = {}) ⇒ Types::DescribeMlflowTrackingServerResponse
Returns information about an MLflow Tracking Server.
-
#describe_model(params = {}) ⇒ Types::DescribeModelOutput
Describes a model that you created using the
CreateModel
API. -
#describe_model_bias_job_definition(params = {}) ⇒ Types::DescribeModelBiasJobDefinitionResponse
Returns a description of a model bias job definition.
-
#describe_model_card(params = {}) ⇒ Types::DescribeModelCardResponse
Describes the content, creation time, and security configuration of an Amazon SageMaker Model Card.
-
#describe_model_card_export_job(params = {}) ⇒ Types::DescribeModelCardExportJobResponse
Describes an Amazon SageMaker Model Card export job.
-
#describe_model_explainability_job_definition(params = {}) ⇒ Types::DescribeModelExplainabilityJobDefinitionResponse
Returns a description of a model explainability job definition.
-
#describe_model_package(params = {}) ⇒ Types::DescribeModelPackageOutput
Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace.
-
#describe_model_package_group(params = {}) ⇒ Types::DescribeModelPackageGroupOutput
Gets a description for the specified model group.
-
#describe_model_quality_job_definition(params = {}) ⇒ Types::DescribeModelQualityJobDefinitionResponse
Returns a description of a model quality job definition.
-
#describe_monitoring_schedule(params = {}) ⇒ Types::DescribeMonitoringScheduleResponse
Describes the schedule for a monitoring job.
-
#describe_notebook_instance(params = {}) ⇒ Types::DescribeNotebookInstanceOutput
Returns information about a notebook instance.
-
#describe_notebook_instance_lifecycle_config(params = {}) ⇒ Types::DescribeNotebookInstanceLifecycleConfigOutput
Returns a description of a notebook instance lifecycle configuration.
-
#describe_optimization_job(params = {}) ⇒ Types::DescribeOptimizationJobResponse
Provides the properties of the specified optimization job.
-
#describe_partner_app(params = {}) ⇒ Types::DescribePartnerAppResponse
Gets information about a SageMaker Partner AI App.
-
#describe_pipeline(params = {}) ⇒ Types::DescribePipelineResponse
Describes the details of a pipeline.
-
#describe_pipeline_definition_for_execution(params = {}) ⇒ Types::DescribePipelineDefinitionForExecutionResponse
Describes the details of an execution's pipeline definition.
-
#describe_pipeline_execution(params = {}) ⇒ Types::DescribePipelineExecutionResponse
Describes the details of a pipeline execution.
-
#describe_processing_job(params = {}) ⇒ Types::DescribeProcessingJobResponse
Returns a description of a processing job.
-
#describe_project(params = {}) ⇒ Types::DescribeProjectOutput
Describes the details of a project.
-
#describe_space(params = {}) ⇒ Types::DescribeSpaceResponse
Describes the space.
-
#describe_studio_lifecycle_config(params = {}) ⇒ Types::DescribeStudioLifecycleConfigResponse
Describes the Amazon SageMaker Studio Lifecycle Configuration.
-
#describe_subscribed_workteam(params = {}) ⇒ Types::DescribeSubscribedWorkteamResponse
Gets information about a work team provided by a vendor.
-
#describe_training_job(params = {}) ⇒ Types::DescribeTrainingJobResponse
Returns information about a training job.
-
#describe_training_plan(params = {}) ⇒ Types::DescribeTrainingPlanResponse
Retrieves detailed information about a specific training plan.
-
#describe_transform_job(params = {}) ⇒ Types::DescribeTransformJobResponse
Returns information about a transform job.
-
#describe_trial(params = {}) ⇒ Types::DescribeTrialResponse
Provides a list of a trial's properties.
-
#describe_trial_component(params = {}) ⇒ Types::DescribeTrialComponentResponse
Provides a list of a trials component's properties.
-
#describe_user_profile(params = {}) ⇒ Types::DescribeUserProfileResponse
Describes a user profile.
-
#describe_workforce(params = {}) ⇒ Types::DescribeWorkforceResponse
Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges ([CIDRs][1]).
-
#describe_workteam(params = {}) ⇒ Types::DescribeWorkteamResponse
Gets information about a specific work team.
-
#disable_sagemaker_servicecatalog_portfolio(params = {}) ⇒ Struct
Disables using Service Catalog in SageMaker.
-
#disassociate_trial_component(params = {}) ⇒ Types::DisassociateTrialComponentResponse
Disassociates a trial component from a trial.
-
#enable_sagemaker_servicecatalog_portfolio(params = {}) ⇒ Struct
Enables using Service Catalog in SageMaker.
-
#get_device_fleet_report(params = {}) ⇒ Types::GetDeviceFleetReportResponse
Describes a fleet.
-
#get_lineage_group_policy(params = {}) ⇒ Types::GetLineageGroupPolicyResponse
The resource policy for the lineage group.
-
#get_model_package_group_policy(params = {}) ⇒ Types::GetModelPackageGroupPolicyOutput
Gets a resource policy that manages access for a model group.
-
#get_sagemaker_servicecatalog_portfolio_status(params = {}) ⇒ Types::GetSagemakerServicecatalogPortfolioStatusOutput
Gets the status of Service Catalog in SageMaker.
-
#get_scaling_configuration_recommendation(params = {}) ⇒ Types::GetScalingConfigurationRecommendationResponse
Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job.
-
#get_search_suggestions(params = {}) ⇒ Types::GetSearchSuggestionsResponse
An auto-complete API for the search functionality in the SageMaker console.
-
#import_hub_content(params = {}) ⇒ Types::ImportHubContentResponse
Import hub content.
-
#list_actions(params = {}) ⇒ Types::ListActionsResponse
Lists the actions in your account and their properties.
-
#list_algorithms(params = {}) ⇒ Types::ListAlgorithmsOutput
Lists the machine learning algorithms that have been created.
-
#list_aliases(params = {}) ⇒ Types::ListAliasesResponse
Lists the aliases of a specified image or image version.
-
#list_app_image_configs(params = {}) ⇒ Types::ListAppImageConfigsResponse
Lists the AppImageConfigs in your account and their properties.
-
#list_apps(params = {}) ⇒ Types::ListAppsResponse
Lists apps.
-
#list_artifacts(params = {}) ⇒ Types::ListArtifactsResponse
Lists the artifacts in your account and their properties.
-
#list_associations(params = {}) ⇒ Types::ListAssociationsResponse
Lists the associations in your account and their properties.
-
#list_auto_ml_jobs(params = {}) ⇒ Types::ListAutoMLJobsResponse
Request a list of jobs.
-
#list_candidates_for_auto_ml_job(params = {}) ⇒ Types::ListCandidatesForAutoMLJobResponse
List the candidates created for the job.
-
#list_cluster_nodes(params = {}) ⇒ Types::ListClusterNodesResponse
Retrieves the list of instances (also called nodes interchangeably) in a SageMaker HyperPod cluster.
-
#list_cluster_scheduler_configs(params = {}) ⇒ Types::ListClusterSchedulerConfigsResponse
List the cluster policy configurations.
-
#list_clusters(params = {}) ⇒ Types::ListClustersResponse
Retrieves the list of SageMaker HyperPod clusters.
-
#list_code_repositories(params = {}) ⇒ Types::ListCodeRepositoriesOutput
Gets a list of the Git repositories in your account.
-
#list_compilation_jobs(params = {}) ⇒ Types::ListCompilationJobsResponse
Lists model compilation jobs that satisfy various filters.
-
#list_compute_quotas(params = {}) ⇒ Types::ListComputeQuotasResponse
List the resource allocation definitions.
-
#list_contexts(params = {}) ⇒ Types::ListContextsResponse
Lists the contexts in your account and their properties.
-
#list_data_quality_job_definitions(params = {}) ⇒ Types::ListDataQualityJobDefinitionsResponse
Lists the data quality job definitions in your account.
-
#list_device_fleets(params = {}) ⇒ Types::ListDeviceFleetsResponse
Returns a list of devices in the fleet.
-
#list_devices(params = {}) ⇒ Types::ListDevicesResponse
A list of devices.
-
#list_domains(params = {}) ⇒ Types::ListDomainsResponse
Lists the domains.
-
#list_edge_deployment_plans(params = {}) ⇒ Types::ListEdgeDeploymentPlansResponse
Lists all edge deployment plans.
-
#list_edge_packaging_jobs(params = {}) ⇒ Types::ListEdgePackagingJobsResponse
Returns a list of edge packaging jobs.
-
#list_endpoint_configs(params = {}) ⇒ Types::ListEndpointConfigsOutput
Lists endpoint configurations.
-
#list_endpoints(params = {}) ⇒ Types::ListEndpointsOutput
Lists endpoints.
-
#list_experiments(params = {}) ⇒ Types::ListExperimentsResponse
Lists all the experiments in your account.
-
#list_feature_groups(params = {}) ⇒ Types::ListFeatureGroupsResponse
List
FeatureGroup
s based on given filter and order. -
#list_flow_definitions(params = {}) ⇒ Types::ListFlowDefinitionsResponse
Returns information about the flow definitions in your account.
-
#list_hub_content_versions(params = {}) ⇒ Types::ListHubContentVersionsResponse
List hub content versions.
-
#list_hub_contents(params = {}) ⇒ Types::ListHubContentsResponse
List the contents of a hub.
-
#list_hubs(params = {}) ⇒ Types::ListHubsResponse
List all existing hubs.
-
#list_human_task_uis(params = {}) ⇒ Types::ListHumanTaskUisResponse
Returns information about the human task user interfaces in your account.
-
#list_hyper_parameter_tuning_jobs(params = {}) ⇒ Types::ListHyperParameterTuningJobsResponse
Gets a list of [HyperParameterTuningJobSummary][1] objects that describe the hyperparameter tuning jobs launched in your account.
-
#list_image_versions(params = {}) ⇒ Types::ListImageVersionsResponse
Lists the versions of a specified image and their properties.
-
#list_images(params = {}) ⇒ Types::ListImagesResponse
Lists the images in your account and their properties.
-
#list_inference_components(params = {}) ⇒ Types::ListInferenceComponentsOutput
Lists the inference components in your account and their properties.
-
#list_inference_experiments(params = {}) ⇒ Types::ListInferenceExperimentsResponse
Returns the list of all inference experiments.
-
#list_inference_recommendations_job_steps(params = {}) ⇒ Types::ListInferenceRecommendationsJobStepsResponse
Returns a list of the subtasks for an Inference Recommender job.
-
#list_inference_recommendations_jobs(params = {}) ⇒ Types::ListInferenceRecommendationsJobsResponse
Lists recommendation jobs that satisfy various filters.
-
#list_labeling_jobs(params = {}) ⇒ Types::ListLabelingJobsResponse
Gets a list of labeling jobs.
-
#list_labeling_jobs_for_workteam(params = {}) ⇒ Types::ListLabelingJobsForWorkteamResponse
Gets a list of labeling jobs assigned to a specified work team.
-
#list_lineage_groups(params = {}) ⇒ Types::ListLineageGroupsResponse
A list of lineage groups shared with your Amazon Web Services account.
-
#list_mlflow_tracking_servers(params = {}) ⇒ Types::ListMlflowTrackingServersResponse
Lists all MLflow Tracking Servers.
-
#list_model_bias_job_definitions(params = {}) ⇒ Types::ListModelBiasJobDefinitionsResponse
Lists model bias jobs definitions that satisfy various filters.
-
#list_model_card_export_jobs(params = {}) ⇒ Types::ListModelCardExportJobsResponse
List the export jobs for the Amazon SageMaker Model Card.
-
#list_model_card_versions(params = {}) ⇒ Types::ListModelCardVersionsResponse
List existing versions of an Amazon SageMaker Model Card.
-
#list_model_cards(params = {}) ⇒ Types::ListModelCardsResponse
List existing model cards.
-
#list_model_explainability_job_definitions(params = {}) ⇒ Types::ListModelExplainabilityJobDefinitionsResponse
Lists model explainability job definitions that satisfy various filters.
-
#list_model_metadata(params = {}) ⇒ Types::ListModelMetadataResponse
Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos.
-
#list_model_package_groups(params = {}) ⇒ Types::ListModelPackageGroupsOutput
Gets a list of the model groups in your Amazon Web Services account.
-
#list_model_packages(params = {}) ⇒ Types::ListModelPackagesOutput
Lists the model packages that have been created.
-
#list_model_quality_job_definitions(params = {}) ⇒ Types::ListModelQualityJobDefinitionsResponse
Gets a list of model quality monitoring job definitions in your account.
-
#list_models(params = {}) ⇒ Types::ListModelsOutput
Lists models created with the
CreateModel
API. -
#list_monitoring_alert_history(params = {}) ⇒ Types::ListMonitoringAlertHistoryResponse
Gets a list of past alerts in a model monitoring schedule.
-
#list_monitoring_alerts(params = {}) ⇒ Types::ListMonitoringAlertsResponse
Gets the alerts for a single monitoring schedule.
-
#list_monitoring_executions(params = {}) ⇒ Types::ListMonitoringExecutionsResponse
Returns list of all monitoring job executions.
-
#list_monitoring_schedules(params = {}) ⇒ Types::ListMonitoringSchedulesResponse
Returns list of all monitoring schedules.
-
#list_notebook_instance_lifecycle_configs(params = {}) ⇒ Types::ListNotebookInstanceLifecycleConfigsOutput
Lists notebook instance lifestyle configurations created with the [CreateNotebookInstanceLifecycleConfig][1] API.
-
#list_notebook_instances(params = {}) ⇒ Types::ListNotebookInstancesOutput
Returns a list of the SageMaker notebook instances in the requester's account in an Amazon Web Services Region.
-
#list_optimization_jobs(params = {}) ⇒ Types::ListOptimizationJobsResponse
Lists the optimization jobs in your account and their properties.
-
#list_partner_apps(params = {}) ⇒ Types::ListPartnerAppsResponse
Lists all of the SageMaker Partner AI Apps in an account.
-
#list_pipeline_execution_steps(params = {}) ⇒ Types::ListPipelineExecutionStepsResponse
Gets a list of
PipeLineExecutionStep
objects. -
#list_pipeline_executions(params = {}) ⇒ Types::ListPipelineExecutionsResponse
Gets a list of the pipeline executions.
-
#list_pipeline_parameters_for_execution(params = {}) ⇒ Types::ListPipelineParametersForExecutionResponse
Gets a list of parameters for a pipeline execution.
-
#list_pipelines(params = {}) ⇒ Types::ListPipelinesResponse
Gets a list of pipelines.
-
#list_processing_jobs(params = {}) ⇒ Types::ListProcessingJobsResponse
Lists processing jobs that satisfy various filters.
-
#list_projects(params = {}) ⇒ Types::ListProjectsOutput
Gets a list of the projects in an Amazon Web Services account.
-
#list_resource_catalogs(params = {}) ⇒ Types::ListResourceCatalogsResponse
Lists Amazon SageMaker Catalogs based on given filters and orders.
-
#list_spaces(params = {}) ⇒ Types::ListSpacesResponse
Lists spaces.
-
#list_stage_devices(params = {}) ⇒ Types::ListStageDevicesResponse
Lists devices allocated to the stage, containing detailed device information and deployment status.
-
#list_studio_lifecycle_configs(params = {}) ⇒ Types::ListStudioLifecycleConfigsResponse
Lists the Amazon SageMaker Studio Lifecycle Configurations in your Amazon Web Services Account.
-
#list_subscribed_workteams(params = {}) ⇒ Types::ListSubscribedWorkteamsResponse
Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace.
-
#list_tags(params = {}) ⇒ Types::ListTagsOutput
Returns the tags for the specified SageMaker resource.
-
#list_training_jobs(params = {}) ⇒ Types::ListTrainingJobsResponse
Lists training jobs.
-
#list_training_jobs_for_hyper_parameter_tuning_job(params = {}) ⇒ Types::ListTrainingJobsForHyperParameterTuningJobResponse
Gets a list of [TrainingJobSummary][1] objects that describe the training jobs that a hyperparameter tuning job launched.
-
#list_training_plans(params = {}) ⇒ Types::ListTrainingPlansResponse
Retrieves a list of training plans for the current account.
-
#list_transform_jobs(params = {}) ⇒ Types::ListTransformJobsResponse
Lists transform jobs.
-
#list_trial_components(params = {}) ⇒ Types::ListTrialComponentsResponse
Lists the trial components in your account.
-
#list_trials(params = {}) ⇒ Types::ListTrialsResponse
Lists the trials in your account.
-
#list_user_profiles(params = {}) ⇒ Types::ListUserProfilesResponse
Lists user profiles.
-
#list_workforces(params = {}) ⇒ Types::ListWorkforcesResponse
Use this operation to list all private and vendor workforces in an Amazon Web Services Region.
-
#list_workteams(params = {}) ⇒ Types::ListWorkteamsResponse
Gets a list of private work teams that you have defined in a region.
-
#put_model_package_group_policy(params = {}) ⇒ Types::PutModelPackageGroupPolicyOutput
Adds a resouce policy to control access to a model group.
-
#query_lineage(params = {}) ⇒ Types::QueryLineageResponse
Use this action to inspect your lineage and discover relationships between entities.
-
#register_devices(params = {}) ⇒ Struct
Register devices.
-
#render_ui_template(params = {}) ⇒ Types::RenderUiTemplateResponse
Renders the UI template so that you can preview the worker's experience.
-
#retry_pipeline_execution(params = {}) ⇒ Types::RetryPipelineExecutionResponse
Retry the execution of the pipeline.
-
#search(params = {}) ⇒ Types::SearchResponse
Finds SageMaker resources that match a search query.
-
#search_training_plan_offerings(params = {}) ⇒ Types::SearchTrainingPlanOfferingsResponse
Searches for available training plan offerings based on specified criteria.
-
#send_pipeline_execution_step_failure(params = {}) ⇒ Types::SendPipelineExecutionStepFailureResponse
Notifies the pipeline that the execution of a callback step failed, along with a message describing why.
-
#send_pipeline_execution_step_success(params = {}) ⇒ Types::SendPipelineExecutionStepSuccessResponse
Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters.
-
#start_edge_deployment_stage(params = {}) ⇒ Struct
Starts a stage in an edge deployment plan.
-
#start_inference_experiment(params = {}) ⇒ Types::StartInferenceExperimentResponse
Starts an inference experiment.
-
#start_mlflow_tracking_server(params = {}) ⇒ Types::StartMlflowTrackingServerResponse
Programmatically start an MLflow Tracking Server.
-
#start_monitoring_schedule(params = {}) ⇒ Struct
Starts a previously stopped monitoring schedule.
-
#start_notebook_instance(params = {}) ⇒ Struct
Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume.
-
#start_pipeline_execution(params = {}) ⇒ Types::StartPipelineExecutionResponse
Starts a pipeline execution.
-
#stop_auto_ml_job(params = {}) ⇒ Struct
A method for forcing a running job to shut down.
-
#stop_compilation_job(params = {}) ⇒ Struct
Stops a model compilation job.
-
#stop_edge_deployment_stage(params = {}) ⇒ Struct
Stops a stage in an edge deployment plan.
-
#stop_edge_packaging_job(params = {}) ⇒ Struct
Request to stop an edge packaging job.
-
#stop_hyper_parameter_tuning_job(params = {}) ⇒ Struct
Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched.
-
#stop_inference_experiment(params = {}) ⇒ Types::StopInferenceExperimentResponse
Stops an inference experiment.
-
#stop_inference_recommendations_job(params = {}) ⇒ Struct
Stops an Inference Recommender job.
-
#stop_labeling_job(params = {}) ⇒ Struct
Stops a running labeling job.
-
#stop_mlflow_tracking_server(params = {}) ⇒ Types::StopMlflowTrackingServerResponse
Programmatically stop an MLflow Tracking Server.
-
#stop_monitoring_schedule(params = {}) ⇒ Struct
Stops a previously started monitoring schedule.
-
#stop_notebook_instance(params = {}) ⇒ Struct
Terminates the ML compute instance.
-
#stop_optimization_job(params = {}) ⇒ Struct
Ends a running inference optimization job.
-
#stop_pipeline_execution(params = {}) ⇒ Types::StopPipelineExecutionResponse
Stops a pipeline execution.
-
#stop_processing_job(params = {}) ⇒ Struct
Stops a processing job.
-
#stop_training_job(params = {}) ⇒ Struct
Stops a training job.
-
#stop_transform_job(params = {}) ⇒ Struct
Stops a batch transform job.
-
#update_action(params = {}) ⇒ Types::UpdateActionResponse
Updates an action.
-
#update_app_image_config(params = {}) ⇒ Types::UpdateAppImageConfigResponse
Updates the properties of an AppImageConfig.
-
#update_artifact(params = {}) ⇒ Types::UpdateArtifactResponse
Updates an artifact.
-
#update_cluster(params = {}) ⇒ Types::UpdateClusterResponse
Updates a SageMaker HyperPod cluster.
-
#update_cluster_scheduler_config(params = {}) ⇒ Types::UpdateClusterSchedulerConfigResponse
Update the cluster policy configuration.
-
#update_cluster_software(params = {}) ⇒ Types::UpdateClusterSoftwareResponse
Updates the platform software of a SageMaker HyperPod cluster for security patching.
-
#update_code_repository(params = {}) ⇒ Types::UpdateCodeRepositoryOutput
Updates the specified Git repository with the specified values.
-
#update_compute_quota(params = {}) ⇒ Types::UpdateComputeQuotaResponse
Update the compute allocation definition.
-
#update_context(params = {}) ⇒ Types::UpdateContextResponse
Updates a context.
-
#update_device_fleet(params = {}) ⇒ Struct
Updates a fleet of devices.
-
#update_devices(params = {}) ⇒ Struct
Updates one or more devices in a fleet.
-
#update_domain(params = {}) ⇒ Types::UpdateDomainResponse
Updates the default settings for new user profiles in the domain.
-
#update_endpoint(params = {}) ⇒ Types::UpdateEndpointOutput
Deploys the
EndpointConfig
specified in the request to a new fleet of instances. -
#update_endpoint_weights_and_capacities(params = {}) ⇒ Types::UpdateEndpointWeightsAndCapacitiesOutput
Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint.
-
#update_experiment(params = {}) ⇒ Types::UpdateExperimentResponse
Adds, updates, or removes the description of an experiment.
-
#update_feature_group(params = {}) ⇒ Types::UpdateFeatureGroupResponse
Updates the feature group by either adding features or updating the online store configuration.
-
#update_feature_metadata(params = {}) ⇒ Struct
Updates the description and parameters of the feature group.
-
#update_hub(params = {}) ⇒ Types::UpdateHubResponse
Update a hub.
-
#update_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_mlflow_tracking_server(params = {}) ⇒ Types::UpdateMlflowTrackingServerResponse
Updates properties of an existing MLflow Tracking Server.
-
#update_model_card(params = {}) ⇒ Types::UpdateModelCardResponse
Update an Amazon SageMaker Model Card.
-
#update_model_package(params = {}) ⇒ Types::UpdateModelPackageOutput
Updates a versioned model.
-
#update_monitoring_alert(params = {}) ⇒ Types::UpdateMonitoringAlertResponse
Update the parameters of a model monitor alert.
-
#update_monitoring_schedule(params = {}) ⇒ Types::UpdateMonitoringScheduleResponse
Updates a previously created schedule.
-
#update_notebook_instance(params = {}) ⇒ Struct
Updates a notebook instance.
-
#update_notebook_instance_lifecycle_config(params = {}) ⇒ Struct
Updates a notebook instance lifecycle configuration created with the [CreateNotebookInstanceLifecycleConfig][1] API.
-
#update_partner_app(params = {}) ⇒ Types::UpdatePartnerAppResponse
Updates all of the SageMaker Partner AI Apps in an account.
-
#update_pipeline(params = {}) ⇒ Types::UpdatePipelineResponse
Updates a pipeline.
-
#update_pipeline_execution(params = {}) ⇒ Types::UpdatePipelineExecutionResponse
Updates a pipeline execution.
-
#update_project(params = {}) ⇒ Types::UpdateProjectOutput
Updates a machine learning (ML) project that is created from a template that sets up an ML pipeline from training to deploying an approved model.
-
#update_space(params = {}) ⇒ Types::UpdateSpaceResponse
Updates the settings of a space.
-
#update_training_job(params = {}) ⇒ Types::UpdateTrainingJobResponse
Update a model training job to request a new Debugger profiling configuration or to change warm pool retention length.
-
#update_trial(params = {}) ⇒ Types::UpdateTrialResponse
Updates the display name of a trial.
-
#update_trial_component(params = {}) ⇒ Types::UpdateTrialComponentResponse
Updates one or more properties of a trial component.
-
#update_user_profile(params = {}) ⇒ Types::UpdateUserProfileResponse
Updates a user profile.
-
#update_workforce(params = {}) ⇒ Types::UpdateWorkforceResponse
Use this operation to update your workforce.
-
#update_workteam(params = {}) ⇒ Types::UpdateWorkteamResponse
Updates an existing work team with new member definitions or description.
Instance Method Summary collapse
-
#initialize(options) ⇒ Client
constructor
A new instance of Client.
-
#wait_until(waiter_name, params = {}, options = {}) {|w.waiter| ... } ⇒ Boolean
Polls an API operation until a resource enters a desired state.
Methods included from ClientStubs
#api_requests, #stub_data, #stub_responses
Methods inherited from Seahorse::Client::Base
add_plugin, api, clear_plugins, define, new, #operation_names, plugins, remove_plugin, set_api, set_plugins
Methods included from Seahorse::Client::HandlerBuilder
#handle, #handle_request, #handle_response
Constructor Details
#initialize(options) ⇒ Client
Returns a new instance of Client.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 451 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 513 def add_association(params = {}, = {}) req = build_request(:add_association, params) req.send_request() end |
#add_tags(params = {}) ⇒ Types::AddTagsOutput
Adds or overwrites one or more tags for the specified SageMaker resource. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints.
Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see Amazon Web Services Tagging Strategies.
Tags
parameter of
CreateHyperParameterTuningJob
Tags
parameter of CreateDomain or CreateUserProfile.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 596 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 636 def associate_trial_component(params = {}, = {}) req = build_request(:associate_trial_component, params) req.send_request() end |
#batch_delete_cluster_nodes(params = {}) ⇒ Types::BatchDeleteClusterNodesResponse
Deletes specific nodes within a SageMaker HyperPod cluster.
BatchDeleteClusterNodes
accepts a cluster name and a list of node
IDs.
To safeguard your work, back up your data to Amazon S3 or an FSx for Lustre file system before invoking the API on a worker node group. This will help prevent any potential data loss from the instance root volume. For more information about backup, see Use the backup script provided by SageMaker HyperPod.
If you want to invoke this API on an existing cluster, you'll first need to patch the cluster by running the UpdateClusterSoftware API. For more information about patching a cluster, see Update the SageMaker HyperPod platform software of a cluster.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 699 def batch_delete_cluster_nodes(params = {}, = {}) req = build_request(:batch_delete_cluster_nodes, params) req.send_request() end |
#batch_describe_model_package(params = {}) ⇒ Types::BatchDescribeModelPackageOutput
This action batch describes a list of versioned model packages
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 767 def batch_describe_model_package(params = {}, = {}) req = build_request(:batch_describe_model_package, params) req.send_request() end |
#create_action(params = {}) ⇒ Types::CreateActionResponse
Creates an action. An action is a lineage tracking entity that represents an action or activity. For example, a model deployment or an HPO job. Generally, an action involves at least one input or output artifact. For more information, see Amazon SageMaker ML Lineage Tracking.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 848 def create_action(params = {}, = {}) req = build_request(:create_action, params) req.send_request() end |
#create_algorithm(params = {}) ⇒ Types::CreateAlgorithmOutput
Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1138 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1217 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1316 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 1392 def create_artifact(params = {}, = {}) req = build_request(:create_artifact, params) req.send_request() end |
#create_auto_ml_job(params = {}) ⇒ Types::CreateAutoMLJobResponse
Creates an Autopilot job also referred to as Autopilot experiment or AutoML job.
An AutoML job in SageMaker is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment.
For more information about AutoML jobs, see https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html in the SageMaker developer guide.
CreateAutoMLJobV2
can manage tabular problem types identical to
those of its previous version CreateAutoMLJob
, as well as
time-series forecasting, non-tabular problem types such as image or
text classification, and text generation (LLMs fine-tuning).
Find guidelines about how to migrate a CreateAutoMLJob
to
CreateAutoMLJobV2
in Migrate a CreateAutoMLJob to
CreateAutoMLJobV2.
You can find the best-performing model after you run an AutoML job by calling DescribeAutoMLJobV2 (recommended) or DescribeAutoMLJob.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1591 def create_auto_ml_job(params = {}, = {}) req = build_request(:create_auto_ml_job, params) req.send_request() end |
#create_auto_ml_job_v2(params = {}) ⇒ Types::CreateAutoMLJobV2Response
Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.
An AutoML job in SageMaker is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment.
For more information about AutoML jobs, see https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html in the SageMaker developer guide.
AutoML jobs V2 support various problem types such as regression, binary, and multiclass classification with tabular data, text and image classification, time-series forecasting, and fine-tuning of large language models (LLMs) for text generation.
CreateAutoMLJobV2
can manage tabular problem types identical to
those of its previous version CreateAutoMLJob
, as well as
time-series forecasting, non-tabular problem types such as image or
text classification, and text generation (LLMs fine-tuning).
Find guidelines about how to migrate a CreateAutoMLJob
to
CreateAutoMLJobV2
in Migrate a CreateAutoMLJob to
CreateAutoMLJobV2.
For the list of available problem types supported by
CreateAutoMLJobV2
, see AutoMLProblemTypeConfig.
You can find the best-performing model after you run an AutoML job V2 by calling DescribeAutoMLJobV2.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1909 def create_auto_ml_job_v2(params = {}, = {}) req = build_request(:create_auto_ml_job_v2, params) req.send_request() end |
#create_cluster(params = {}) ⇒ Types::CreateClusterResponse
Creates a SageMaker HyperPod cluster. SageMaker HyperPod is a capability of SageMaker for creating and managing persistent clusters for developing large machine learning models, such as large language models (LLMs) and diffusion models. To learn more, see Amazon SageMaker HyperPod in the Amazon SageMaker Developer Guide.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2024 def create_cluster(params = {}, = {}) req = build_request(:create_cluster, params) req.send_request() end |
#create_cluster_scheduler_config(params = {}) ⇒ Types::CreateClusterSchedulerConfigResponse
Create cluster policy configuration. This policy is used for task prioritization and fair-share allocation of idle compute. This helps prioritize critical workloads and distributes idle compute across entities.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2086 def create_cluster_scheduler_config(params = {}, = {}) req = build_request(:create_cluster_scheduler_config, params) req.send_request() end |
#create_code_repository(params = {}) ⇒ Types::CreateCodeRepositoryOutput
Creates a Git repository as a resource in your SageMaker 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2154 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2316 def create_compilation_job(params = {}, = {}) req = build_request(:create_compilation_job, params) req.send_request() end |
#create_compute_quota(params = {}) ⇒ Types::CreateComputeQuotaResponse
Create compute allocation definition. This defines how compute is allocated, shared, and borrowed for specified entities. Specifically, how to lend and borrow idle compute and assign a fair-share weight to the specified entities.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2398 def create_compute_quota(params = {}, = {}) req = build_request(:create_compute_quota, params) req.send_request() end |
#create_context(params = {}) ⇒ Types::CreateContextResponse
Creates a context. A context is a lineage tracking entity that represents a logical grouping of other tracking or experiment entities. Some examples are an endpoint and a model package. For more information, see Amazon SageMaker ML Lineage Tracking.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2465 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 2630 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 2689 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3165 def create_domain(params = {}, = {}) req = build_request(:create_domain, params) req.send_request() end |
#create_edge_deployment_plan(params = {}) ⇒ Types::CreateEdgeDeploymentPlanResponse
Creates an edge deployment plan, consisting of multiple stages. Each stage may have a different deployment configuration and devices.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3234 def create_edge_deployment_plan(params = {}, = {}) req = build_request(:create_edge_deployment_plan, params) req.send_request() end |
#create_edge_deployment_stage(params = {}) ⇒ Struct
Creates a new stage in an existing edge deployment plan.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3273 def create_edge_deployment_stage(params = {}, = {}) req = build_request(:create_edge_deployment_stage, params) req.send_request() end |
#create_edge_packaging_job(params = {}) ⇒ Struct
Starts a SageMaker Edge Manager model packaging job. Edge Manager will use the model artifacts from the Amazon Simple Storage Service bucket that you specify. After the model has been packaged, Amazon SageMaker saves the resulting artifacts to an S3 bucket that you specify.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3340 def create_edge_packaging_job(params = {}, = {}) req = build_request(:create_edge_packaging_job, params) req.send_request() end |
#create_endpoint(params = {}) ⇒ Types::CreateEndpointOutput
Creates an endpoint using the endpoint configuration specified in the request. SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.
Use this API to deploy models using SageMaker hosting services.
EndpointConfig
that is in use by an endpoint
that is live or while the UpdateEndpoint
or CreateEndpoint
operations are being performed on the endpoint. To update an endpoint,
you must create a new EndpointConfig
.
The endpoint name must be unique within an Amazon Web Services Region in your Amazon Web Services account.
When it receives the request, SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.
Eventually Consistent Reads
,
the response might not reflect the results of a recently completed
write operation. The response might include some stale data. If the
dependent entities are not yet in DynamoDB, this causes a validation
error. If you repeat your read request after a short time, the
response should return the latest data. So retry logic is recommended
to handle these possible issues. We also recommend that customers call
DescribeEndpointConfig before calling CreateEndpoint to
minimize the potential impact of a DynamoDB eventually consistent
read.
When SageMaker receives the request, it sets the endpoint status to
Creating
. After it creates the endpoint, it sets the status to
InService
. SageMaker can then process incoming requests for
inferences. To check the status of an endpoint, use the
DescribeEndpoint API.
If any of the models hosted at this endpoint get model data from an Amazon S3 location, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.
Option 1: For a full SageMaker access, search and attach the
AmazonSageMakerFullAccess
policy.Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the JSON file of the IAM role:
"Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"]
"Resource": [
"arn:aws:sagemaker:region:account-id:endpoint/endpointName"
"arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName"
]
For more information, see SageMaker API Permissions: Actions, Permissions, and Resources Reference.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3531 def create_endpoint(params = {}, = {}) req = build_request(:create_endpoint, params) req.send_request() end |
#create_endpoint_config(params = {}) ⇒ Types::CreateEndpointConfigOutput
Creates an endpoint configuration that SageMaker hosting services uses
to deploy models. In the configuration, you identify one or more
models, created using the CreateModel
API, to deploy and the
resources that you want SageMaker to provision. Then you call the
CreateEndpoint API.
In the request, you define a ProductionVariant
, for each model that
you want to deploy. Each ProductionVariant
parameter also describes
the resources that you want SageMaker to provision. This includes the
number and type of ML compute instances to deploy.
If you are hosting multiple models, you also assign a VariantWeight
to specify how much traffic you want to allocate to each model. For
example, suppose that you want to host two models, A and B, and you
assign traffic weight 2 for model A and 1 for model B. SageMaker
distributes two-thirds of the traffic to Model A, and one-third to
model B.
Eventually Consistent Reads
,
the response might not reflect the results of a recently completed
write operation. The response might include some stale data. If the
dependent entities are not yet in DynamoDB, this causes a validation
error. If you repeat your read request after a short time, the
response should return the latest data. So retry logic is recommended
to handle these possible issues. We also recommend that customers call
DescribeEndpointConfig before calling CreateEndpoint to
minimize the potential impact of a DynamoDB eventually consistent
read.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3857 def create_endpoint_config(params = {}, = {}) req = build_request(:create_endpoint_config, params) req.send_request() end |
#create_experiment(params = {}) ⇒ Types::CreateExperimentResponse
Creates a SageMaker experiment. An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called trial components, that produce a machine learning model.
The goal of an experiment is to determine the components that produce the best model. Multiple trials are performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the remaining inputs constant.
When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to experiments, trials, trial components and then use the Search API to search for the tags.
To add a description to an experiment, specify the optional
Description
parameter. To add a description later, or to change the
description, call the UpdateExperiment API.
To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To create a trial call the CreateTrial API.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3950 def create_experiment(params = {}, = {}) req = build_request(:create_experiment, params) req.send_request() end |
#create_feature_group(params = {}) ⇒ Types::CreateFeatureGroupResponse
Create a new FeatureGroup
. A FeatureGroup
is a group of Features
defined in the FeatureStore
to describe a Record
.
The FeatureGroup
defines the schema and features contained in the
FeatureGroup
. A FeatureGroup
definition is composed of a list of
Features
, a RecordIdentifierFeatureName
, an EventTimeFeatureName
and configurations for its OnlineStore
and OfflineStore
. Check
Amazon Web Services service quotas to see the FeatureGroup
s
quota for your Amazon Web Services account.
Note that it can take approximately 10-15 minutes to provision an
OnlineStore
FeatureGroup
with the InMemory
StorageType
.
You must include at least one of OnlineStoreConfig
and
OfflineStoreConfig
to create a FeatureGroup
.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4175 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 4266 def create_flow_definition(params = {}, = {}) req = build_request(:create_flow_definition, params) req.send_request() end |
#create_hub(params = {}) ⇒ Types::CreateHubResponse
Create a hub.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4321 def create_hub(params = {}, = {}) req = build_request(:create_hub, params) req.send_request() end |
#create_hub_content_reference(params = {}) ⇒ Types::CreateHubContentReferenceResponse
Create a hub content reference in order to add a model in the JumpStart public hub to a private hub.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4373 def create_hub_content_reference(params = {}, = {}) req = build_request(:create_hub_content_reference, params) req.send_request() end |
#create_human_task_ui(params = {}) ⇒ Types::CreateHumanTaskUiResponse
Defines the settings you will use for the human review workflow user interface. Reviewers will see a three-panel interface with an instruction area, the item to review, and an input area.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4420 def create_human_task_ui(params = {}, = {}) req = build_request(:create_human_task_ui, params) req.send_request() end |
#create_hyper_parameter_tuning_job(params = {}) ⇒ Types::CreateHyperParameterTuningJobResponse
Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.
A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and trial components for each training job that it runs. You can view these entities in Amazon SageMaker Studio. For more information, see View Experiments, Trials, and Trial Components.
Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4919 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4977 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
.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5082 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.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5177 def create_inference_component(params = {}, = {}) req = build_request(:create_inference_component, params) req.send_request() end |
#create_inference_experiment(params = {}) ⇒ Types::CreateInferenceExperimentResponse
Creates an inference experiment using the configurations specified in the request.
Use this API to setup and schedule an experiment to compare model variants on a Amazon SageMaker inference endpoint. For more information about inference experiments, see Shadow tests.
Amazon SageMaker begins your experiment at the scheduled time and routes traffic to your endpoint's model variants based on your specified configuration.
While the experiment is in progress or after it has concluded, you can view metrics that compare your model variants. For more information, see View, monitor, and edit shadow tests.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5376 def create_inference_experiment(params = {}, = {}) req = build_request(:create_inference_experiment, params) req.send_request() end |
#create_inference_recommendations_job(params = {}) ⇒ Types::CreateInferenceRecommendationsJobResponse
Starts a recommendation job. You can create either an instance recommendation or load test job.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5539 def create_inference_recommendations_job(params = {}, = {}) req = build_request(:create_inference_recommendations_job, params) req.send_request() end |
#create_labeling_job(params = {}) ⇒ Types::CreateLabelingJobResponse
Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models.
You can select your workforce from one of three providers:
A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required.
One or more vendors that you select from the Amazon Web Services Marketplace. Vendors provide expertise in specific areas.
The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information.
You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling.
The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data.
The output can be used as the manifest file for another labeling job or as training data for your machine learning models.
You can use this operation to create a static labeling job or a
streaming labeling job. A static labeling job stops if all data
objects in the input manifest file identified in ManifestS3Uri
have
been labeled. A streaming labeling job runs perpetually until it is
manually stopped, or remains idle for 10 days. You can send new data
objects to an active (InProgress
) streaming labeling job in real
time. To learn how to create a static labeling job, see Create a
Labeling Job (API) in the Amazon SageMaker Developer Guide. To
learn how to create a streaming labeling job, see Create a Streaming
Labeling Job.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5848 def create_labeling_job(params = {}, = {}) req = build_request(:create_labeling_job, params) req.send_request() end |
#create_mlflow_tracking_server(params = {}) ⇒ Types::CreateMlflowTrackingServerResponse
Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store. For more information, see Create an MLflow Tracking Server.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5945 def create_mlflow_tracking_server(params = {}, = {}) req = build_request(:create_mlflow_tracking_server, params) req.send_request() end |
#create_model(params = {}) ⇒ Types::CreateModelOutput
Creates a model in SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions.
Use this API to create a model if you want to use SageMaker hosting services or run a batch transform job.
To host your model, you create an endpoint configuration with the
CreateEndpointConfig
API, and then create an endpoint with the
CreateEndpoint
API. SageMaker then deploys all of the containers
that you defined for the model in the hosting environment.
To run a batch transform using your model, you start a job with the
CreateTransformJob
API. SageMaker uses your model and your dataset
to get inferences which are then saved to a specified S3 location.
In the request, you also provide an IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other Amazon Web Services resources, you grant necessary permissions via this role.