sagemaker: Amazon SageMaker Service

Description Usage Arguments Value Service syntax Operations Examples

View source: R/sagemaker_service.R

Description

Provides APIs for creating and managing Amazon SageMaker resources.

Other Resources:

Usage

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sagemaker(config = list())

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

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svc <- sagemaker(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string"
    ),
    endpoint = "string",
    region = "string"
  )
)

Operations

add_association Creates an association between the source and the destination
add_tags Adds or overwrites one or more tags for the specified Amazon SageMaker resource
associate_trial_component Associates a trial component with a trial
create_action Creates an action
create_algorithm Create a machine learning algorithm that you can use in Amazon SageMaker and list in the AWS Marketplace
create_app Creates a running App for the specified UserProfile
create_app_image_config Creates a configuration for running a SageMaker image as a KernelGateway app
create_artifact Creates an artifact
create_auto_ml_job Creates an Autopilot job
create_code_repository Creates a Git repository as a resource in your Amazon SageMaker account
create_compilation_job Starts a model compilation job
create_context Creates a context
create_data_quality_job_definition Creates a definition for a job that monitors data quality and drift
create_device_fleet Creates a device fleet
create_domain Creates a Domain used by Amazon SageMaker Studio
create_edge_packaging_job Starts a SageMaker Edge Manager model packaging job
create_endpoint Creates an endpoint using the endpoint configuration specified in the request
create_endpoint_config Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models
create_experiment Creates an SageMaker experiment
create_feature_group Create a new FeatureGroup
create_flow_definition Creates a flow definition
create_human_task_ui Defines the settings you will use for the human review workflow user interface
create_hyper_parameter_tuning_job Starts a hyperparameter tuning job
create_image Creates a custom SageMaker image
create_image_version Creates a version of the SageMaker image specified by ImageName
create_labeling_job Creates a job that uses workers to label the data objects in your input dataset
create_model Creates a model in Amazon SageMaker
create_model_bias_job_definition Creates the definition for a model bias job
create_model_explainability_job_definition Creates the definition for a model explainability job
create_model_package 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 Creates a model group
create_model_quality_job_definition Creates a definition for a job that monitors model quality and drift
create_monitoring_schedule Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endoint
create_notebook_instance Creates an Amazon SageMaker notebook instance
create_notebook_instance_lifecycle_config Creates a lifecycle configuration that you can associate with a notebook instance
create_pipeline Creates a pipeline using a JSON pipeline definition
create_presigned_domain_url Creates a URL for a specified UserProfile in a Domain
create_presigned_notebook_instance_url Returns a URL that you can use to connect to the Jupyter server from a notebook instance
create_processing_job Creates a processing job
create_project 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 Starts a model training job
create_transform_job Starts a transform job
create_trial Creates an Amazon SageMaker trial
create_trial_component Creates a trial component, which is a stage of a machine learning trial
create_user_profile Creates a user profile
create_workforce Use this operation to create a workforce
create_workteam Creates a new work team for labeling your data
delete_action Deletes an action
delete_algorithm Removes the specified algorithm from your account
delete_app Used to stop and delete an app
delete_app_image_config Deletes an AppImageConfig
delete_artifact Deletes an artifact
delete_association Deletes an association
delete_code_repository Deletes the specified Git repository from your account
delete_context Deletes an context
delete_data_quality_job_definition Deletes a data quality monitoring job definition
delete_device_fleet Deletes a fleet
delete_domain Used to delete a domain
delete_endpoint Deletes an endpoint
delete_endpoint_config Deletes an endpoint configuration
delete_experiment Deletes an Amazon SageMaker experiment
delete_feature_group Delete the FeatureGroup and any data that was written to the OnlineStore of the FeatureGroup
delete_flow_definition Deletes the specified flow definition
delete_human_task_ui Use this operation to delete a human task user interface (worker task template)
delete_image Deletes a SageMaker image and all versions of the image
delete_image_version Deletes a version of a SageMaker image
delete_model Deletes a model
delete_model_bias_job_definition Deletes an Amazon SageMaker model bias job definition
delete_model_explainability_job_definition Deletes an Amazon SageMaker model explainability job definition
delete_model_package Deletes a model package
delete_model_package_group Deletes the specified model group
delete_model_package_group_policy Deletes a model group resource policy
delete_model_quality_job_definition Deletes the secified model quality monitoring job definition
delete_monitoring_schedule Deletes a monitoring schedule
delete_notebook_instance Deletes an Amazon SageMaker notebook instance
delete_notebook_instance_lifecycle_config Deletes a notebook instance lifecycle configuration
delete_pipeline Deletes a pipeline if there are no in-progress executions
delete_project Delete the specified project
delete_tags Deletes the specified tags from an Amazon SageMaker resource
delete_trial Deletes the specified trial
delete_trial_component Deletes the specified trial component
delete_user_profile Deletes a user profile
delete_workforce Use this operation to delete a workforce
delete_workteam Deletes an existing work team
deregister_devices Deregisters the specified devices
describe_action Describes an action
describe_algorithm Returns a description of the specified algorithm that is in your account
describe_app Describes the app
describe_app_image_config Describes an AppImageConfig
describe_artifact Describes an artifact
describe_auto_ml_job Returns information about an Amazon SageMaker job
describe_code_repository Gets details about the specified Git repository
describe_compilation_job Returns information about a model compilation job
describe_context Describes a context
describe_data_quality_job_definition Gets the details of a data quality monitoring job definition
describe_device Describes the device
describe_device_fleet A description of the fleet the device belongs to
describe_domain The description of the domain
describe_edge_packaging_job A description of edge packaging jobs
describe_endpoint Returns the description of an endpoint
describe_endpoint_config Returns the description of an endpoint configuration created using the CreateEndpointConfig API
describe_experiment Provides a list of an experiment's properties
describe_feature_group Use this operation to describe a FeatureGroup
describe_flow_definition Returns information about the specified flow definition
describe_human_task_ui Returns information about the requested human task user interface (worker task template)
describe_hyper_parameter_tuning_job Gets a description of a hyperparameter tuning job
describe_image Describes a SageMaker image
describe_image_version Describes a version of a SageMaker image
describe_labeling_job Gets information about a labeling job
describe_model Describes a model that you created using the CreateModel API
describe_model_bias_job_definition Returns a description of a model bias job definition
describe_model_explainability_job_definition Returns a description of a model explainability job definition
describe_model_package 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 Gets a description for the specified model group
describe_model_quality_job_definition Returns a description of a model quality job definition
describe_monitoring_schedule Describes the schedule for a monitoring job
describe_notebook_instance Returns information about a notebook instance
describe_notebook_instance_lifecycle_config Returns a description of a notebook instance lifecycle configuration
describe_pipeline Describes the details of a pipeline
describe_pipeline_definition_for_execution Describes the details of an execution's pipeline definition
describe_pipeline_execution Describes the details of a pipeline execution
describe_processing_job Returns a description of a processing job
describe_project Describes the details of a project
describe_subscribed_workteam Gets information about a work team provided by a vendor
describe_training_job Returns information about a training job
describe_transform_job Returns information about a transform job
describe_trial Provides a list of a trial's properties
describe_trial_component Provides a list of a trials component's properties
describe_user_profile Describes a user profile
describe_workforce Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs)
describe_workteam Gets information about a specific work team
disable_sagemaker_servicecatalog_portfolio Disables using Service Catalog in SageMaker
disassociate_trial_component Disassociates a trial component from a trial
enable_sagemaker_servicecatalog_portfolio Enables using Service Catalog in SageMaker
get_device_fleet_report Describes a fleet
get_model_package_group_policy Gets a resource policy that manages access for a model group
get_sagemaker_servicecatalog_portfolio_status Gets the status of Service Catalog in SageMaker
get_search_suggestions An auto-complete API for the search functionality in the Amazon SageMaker console
list_actions Lists the actions in your account and their properties
list_algorithms Lists the machine learning algorithms that have been created
list_app_image_configs Lists the AppImageConfigs in your account and their properties
list_apps Lists apps
list_artifacts Lists the artifacts in your account and their properties
list_associations Lists the associations in your account and their properties
list_auto_ml_jobs Request a list of jobs
list_candidates_for_auto_ml_job List the Candidates created for the job
list_code_repositories Gets a list of the Git repositories in your account
list_compilation_jobs Lists model compilation jobs that satisfy various filters
list_contexts Lists the contexts in your account and their properties
list_data_quality_job_definitions Lists the data quality job definitions in your account
list_device_fleets Returns a list of devices in the fleet
list_devices A list of devices
list_domains Lists the domains
list_edge_packaging_jobs Returns a list of edge packaging jobs
list_endpoint_configs Lists endpoint configurations
list_endpoints Lists endpoints
list_experiments Lists all the experiments in your account
list_feature_groups List FeatureGroups based on given filter and order
list_flow_definitions Returns information about the flow definitions in your account
list_human_task_uis Returns information about the human task user interfaces in your account
list_hyper_parameter_tuning_jobs Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account
list_images Lists the images in your account and their properties
list_image_versions Lists the versions of a specified image and their properties
list_labeling_jobs Gets a list of labeling jobs
list_labeling_jobs_for_workteam Gets a list of labeling jobs assigned to a specified work team
list_model_bias_job_definitions Lists model bias jobs definitions that satisfy various filters
list_model_explainability_job_definitions Lists model explainability job definitions that satisfy various filters
list_model_package_groups Gets a list of the model groups in your AWS account
list_model_packages Lists the model packages that have been created
list_model_quality_job_definitions Gets a list of model quality monitoring job definitions in your account
list_models Lists models created with the CreateModel API
list_monitoring_executions Returns list of all monitoring job executions
list_monitoring_schedules Returns list of all monitoring schedules
list_notebook_instance_lifecycle_configs Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API
list_notebook_instances Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region
list_pipeline_executions Gets a list of the pipeline executions
list_pipeline_execution_steps Gets a list of PipeLineExecutionStep objects
list_pipeline_parameters_for_execution Gets a list of parameters for a pipeline execution
list_pipelines Gets a list of pipelines
list_processing_jobs Lists processing jobs that satisfy various filters
list_projects Gets a list of the projects in an AWS account
list_subscribed_workteams Gets a list of the work teams that you are subscribed to in the AWS Marketplace
list_tags Returns the tags for the specified Amazon SageMaker resource
list_training_jobs Lists training jobs
list_training_jobs_for_hyper_parameter_tuning_job Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched
list_transform_jobs Lists transform jobs
list_trial_components Lists the trial components in your account
list_trials Lists the trials in your account
list_user_profiles Lists user profiles
list_workforces Use this operation to list all private and vendor workforces in an AWS Region
list_workteams Gets a list of private work teams that you have defined in a region
put_model_package_group_policy Adds a resouce policy to control access to a model group
register_devices Register devices
render_ui_template Renders the UI template so that you can preview the worker's experience
search Finds Amazon SageMaker resources that match a search query
start_monitoring_schedule Starts a previously stopped monitoring schedule
start_notebook_instance Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume
start_pipeline_execution Starts a pipeline execution
stop_auto_ml_job A method for forcing the termination of a running job
stop_compilation_job Stops a model compilation job
stop_edge_packaging_job Request to stop an edge packaging job
stop_hyper_parameter_tuning_job Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched
stop_labeling_job Stops a running labeling job
stop_monitoring_schedule Stops a previously started monitoring schedule
stop_notebook_instance Terminates the ML compute instance
stop_pipeline_execution Stops a pipeline execution
stop_processing_job Stops a processing job
stop_training_job Stops a training job
stop_transform_job Stops a transform job
update_action Updates an action
update_app_image_config Updates the properties of an AppImageConfig
update_artifact Updates an artifact
update_code_repository Updates the specified Git repository with the specified values
update_context Updates a context
update_device_fleet Updates a fleet of devices
update_devices Updates one or more devices in a fleet
update_domain Updates the default settings for new user profiles in the domain
update_endpoint Deploys the new EndpointConfig specified in the request, switches to using newly created endpoint, and then deletes resources provisioned for the endpoint using the previous EndpointConfig (there is no availability loss)
update_endpoint_weights_and_capacities 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 Adds, updates, or removes the description of an experiment
update_image Updates the properties of a SageMaker image
update_model_package Updates a versioned model
update_monitoring_schedule Updates a previously created schedule
update_notebook_instance Updates a notebook instance
update_notebook_instance_lifecycle_config Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API
update_pipeline Updates a pipeline
update_pipeline_execution Updates a pipeline execution
update_training_job Update a model training job to request a new Debugger profiling configuration
update_trial Updates the display name of a trial
update_trial_component Updates one or more properties of a trial component
update_user_profile Updates a user profile
update_workforce Use this operation to update your workforce
update_workteam Updates an existing work team with new member definitions or description

Examples

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## Not run: 
svc <- sagemaker()
svc$add_association(
  Foo = 123
)

## End(Not run)

paws.machine.learning documentation built on Aug. 23, 2021, 9:14 a.m.