sagemaker {paws.machine.learning}

Amazon SageMaker Service

Description

Provides APIs for creating and managing SageMaker resources.

Other Resources:

• SageMaker Developer Guide

• Amazon Augmented AI Runtime API Reference

Usage

sagemaker(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config

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

• credentials:

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

• endpoint: The complete URL to use for the constructed client.

• region: The AWS Region used in instantiating the client.

• close_connection: Immediately close all HTTP connections.

• timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

• s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

• sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

• creds:

  • access_key_id: AWS access key ID

  • secret_access_key: AWS secret access key

  • session_token: AWS temporary session token

• profile: The name of a profile to use. If not given, then the default profile is used.

• anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

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

svc <- sagemaker(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  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 SageMaker resource
associate_trial_component	Associates a trial component with a trial
batch_describe_model_package	This action batch describes a list of versioned model packages
create_action	Creates an action
create_algorithm	Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services 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 also referred to as Autopilot experiment or AutoML job
create_auto_ml_job_v2	Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2
create_cluster	Creates a SageMaker HyperPod cluster
create_code_repository	Creates a Git repository as a resource in your 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
create_edge_deployment_plan	Creates an edge deployment plan, consisting of multiple stages
create_edge_deployment_stage	Creates a new stage in an existing edge deployment plan
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 SageMaker hosting services uses to deploy models
create_experiment	Creates a SageMaker experiment
create_feature_group	Create a new FeatureGroup
create_flow_definition	Creates a flow definition
create_hub	Create a hub
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_inference_component	Creates an inference component, which is a SageMaker hosting object that you can use to deploy a model to an endpoint
create_inference_experiment	Creates an inference experiment using the configurations specified in the request
create_inference_recommendations_job	Starts a recommendation job
create_labeling_job	Creates a job that uses workers to label the data objects in your input dataset
create_model	Creates a model in SageMaker
create_model_bias_job_definition	Creates the definition for a model bias job
create_model_card	Creates an Amazon SageMaker Model Card
create_model_card_export_job	Creates an Amazon SageMaker Model Card export 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 SageMaker models or list on Amazon Web Services 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 Endpoint
create_notebook_instance	Creates an 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_space	Creates a space used for real time collaboration in a domain
create_studio_lifecycle_config	Creates a new Amazon SageMaker Studio Lifecycle Configuration
create_training_job	Starts a model training job
create_transform_job	Starts a transform job
create_trial	Creates an 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_cluster	Delete a SageMaker HyperPod cluster
delete_code_repository	Deletes the specified Git repository from your account
delete_compilation_job	Deletes the specified compilation job
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_edge_deployment_plan	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	Delete a stage in an edge deployment plan if (and only if) the stage is inactive
delete_endpoint	Deletes an endpoint
delete_endpoint_config	Deletes an endpoint configuration
delete_experiment	Deletes an 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_hub	Delete a hub
delete_hub_content	Delete the contents of a hub
delete_human_task_ui	Use this operation to delete a human task user interface (worker task template)
delete_hyper_parameter_tuning_job	Deletes a hyperparameter tuning job
delete_image	Deletes a SageMaker image and all versions of the image
delete_image_version	Deletes a version of a SageMaker image
delete_inference_component	Deletes an inference component
delete_inference_experiment	Deletes an inference experiment
delete_model	Deletes a model
delete_model_bias_job_definition	Deletes an Amazon SageMaker model bias job definition
delete_model_card	Deletes an Amazon SageMaker Model Card
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 SageMaker notebook instance
delete_notebook_instance_lifecycle_config	Deletes a notebook instance lifecycle configuration
delete_pipeline	Deletes a pipeline if there are no running instances of the pipeline
delete_project	Delete the specified project
delete_space	Used to delete a space
delete_studio_lifecycle_config	Deletes the Amazon SageMaker Studio Lifecycle Configuration
delete_tags	Deletes the specified tags from an 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 AutoML job created by calling CreateAutoMLJob
describe_auto_ml_job_v2	Returns information about an AutoML job created by calling CreateAutoMLJobV2 or CreateAutoMLJob
describe_cluster	Retrieves information of a SageMaker HyperPod cluster
describe_cluster_node	Retrieves information of an instance (also called a node interchangeably) of a SageMaker HyperPod cluster
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_deployment_plan	Describes an edge deployment plan with deployment status per stage
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_feature_metadata	Shows the metadata for a feature within a feature group
describe_flow_definition	Returns information about the specified flow definition
describe_hub	Describe a hub
describe_hub_content	Describe the content of a hub
describe_human_task_ui	Returns information about the requested human task user interface (worker task template)
describe_hyper_parameter_tuning_job	Returns a description of a hyperparameter tuning job, depending on the fields selected
describe_image	Describes a SageMaker image
describe_image_version	Describes a version of a SageMaker image
describe_inference_component	Returns information about an inference component
describe_inference_experiment	Returns details about an inference experiment
describe_inference_recommendations_job	Provides the results of the Inference Recommender job
describe_labeling_job	Gets information about a labeling job
describe_lineage_group	Provides a list of properties for the requested lineage group
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_card	Describes the content, creation time, and security configuration of an Amazon SageMaker Model Card
describe_model_card_export_job	Describes an Amazon SageMaker Model Card export job
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 SageMaker models or list them on Amazon Web Services 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_space	Describes the space
describe_studio_lifecycle_config	Describes the Amazon SageMaker Studio Lifecycle Configuration
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_lineage_group_policy	The resource policy for the lineage group
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_scaling_configuration_recommendation	Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job
get_search_suggestions	An auto-complete API for the search functionality in the SageMaker console
import_hub_content	Import hub content
list_actions	Lists the actions in your account and their properties
list_algorithms	Lists the machine learning algorithms that have been created
list_aliases	Lists the aliases of a specified image or image version
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_cluster_nodes	Retrieves the list of instances (also called nodes interchangeably) in a SageMaker HyperPod cluster
list_clusters	Retrieves the list of SageMaker HyperPod clusters
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_deployment_plans	Lists all edge deployment plans
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_hub_contents	List the contents of a hub
list_hub_content_versions	List hub content versions
list_hubs	List all existing hubs
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_inference_components	Lists the inference components in your account and their properties
list_inference_experiments	Returns the list of all inference experiments
list_inference_recommendations_jobs	Lists recommendation jobs that satisfy various filters
list_inference_recommendations_job_steps	Returns a list of the subtasks for an Inference Recommender job
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_lineage_groups	A list of lineage groups shared with your Amazon Web Services account
list_model_bias_job_definitions	Lists model bias jobs definitions that satisfy various filters
list_model_card_export_jobs	List the export jobs for the Amazon SageMaker Model Card
list_model_cards	List existing model cards
list_model_card_versions	List existing versions of an Amazon SageMaker Model Card
list_model_explainability_job_definitions	Lists model explainability job definitions that satisfy various filters
list_model_metadata	Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos
list_model_package_groups	Gets a list of the model groups in your Amazon Web Services 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_alert_history	Gets a list of past alerts in a model monitoring schedule
list_monitoring_alerts	Gets the alerts for a single monitoring schedule
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 SageMaker notebook instances in the requester's account in an Amazon Web Services 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 Amazon Web Services account
list_resource_catalogs	Lists Amazon SageMaker Catalogs based on given filters and orders
list_spaces	Lists spaces
list_stage_devices	Lists devices allocated to the stage, containing detailed device information and deployment status
list_studio_lifecycle_configs	Lists the Amazon SageMaker Studio Lifecycle Configurations in your Amazon Web Services Account
list_subscribed_workteams	Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace
list_tags	Returns the tags for the specified 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 Amazon Web Services 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
query_lineage	Use this action to inspect your lineage and discover relationships between entities
register_devices	Register devices
render_ui_template	Renders the UI template so that you can preview the worker's experience
retry_pipeline_execution	Retry the execution of the pipeline
search	Finds SageMaker resources that match a search query
send_pipeline_execution_step_failure	Notifies the pipeline that the execution of a callback step failed, along with a message describing why
send_pipeline_execution_step_success	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	Starts a stage in an edge deployment plan
start_inference_experiment	Starts an inference experiment
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 a running job to shut down
stop_compilation_job	Stops a model compilation job
stop_edge_deployment_stage	Stops a stage in an edge deployment plan
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_inference_experiment	Stops an inference experiment
stop_inference_recommendations_job	Stops an Inference Recommender job
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 batch transform job
update_action	Updates an action
update_app_image_config	Updates the properties of an AppImageConfig
update_artifact	Updates an artifact
update_cluster	Updates a SageMaker HyperPod cluster
update_cluster_software	Updates the platform software of a SageMaker HyperPod cluster for security patching
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 EndpointConfig specified in the request to a new fleet of instances
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_feature_group	Updates the feature group by either adding features or updating the online store configuration
update_feature_metadata	Updates the description and parameters of the feature group
update_hub	Update a hub
update_image	Updates the properties of a SageMaker image
update_image_version	Updates the properties of a SageMaker image version
update_inference_component	Updates an inference component
update_inference_component_runtime_config	Runtime settings for a model that is deployed with an inference component
update_inference_experiment	Updates an inference experiment that you created
update_model_card	Update an Amazon SageMaker Model Card
update_model_package	Updates a versioned model
update_monitoring_alert	Update the parameters of a model monitor alert
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_project	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	Updates the settings of a space
update_training_job	Update a model training job to request a new Debugger profiling configuration or to change warm pool retention length
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

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

## End(Not run)