sagemaker: Amazon SageMaker Service

Description

Provides APIs for creating and managing SageMaker resources.

Other Resources:

Usage

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

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.

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.

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_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 used by Amazon SageMaker Studio
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_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 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_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_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_image	Deletes a SageMaker image and all versions of the image
delete_image_version	Deletes a version of a SageMaker image
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 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_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_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 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_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_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 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_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_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_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

Run this code

if (FALSE) {
svc <- sagemaker()
svc$add_association(
  Foo = 123
)
}

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