Client

sagemaker

R Documentation

Amazon SageMaker Service¶

Description¶

Provides APIs for creating and managing SageMaker resources.

Other Resources:

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; 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; 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; 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_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; 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; 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; 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; 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; Returns a description of a notebook instance lifecycle configuration
describe_pipeline: Describes the details of a pipeline; 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; Disables using Service Catalog in SageMaker
disassociate_trial_component: Disassociates a trial component from a trial; 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; Gets the status of Service Catalog in SageMaker; 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; 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; 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; 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; 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; 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: Update a SageMaker HyperPod cluster
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); 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; 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; 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)