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_hub_content_reference: Create a hub content reference in order to add a model in the JumpStart public hub to a private 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_mlflow_tracking_server: Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store
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_optimization_job: Creates a job that optimizes a model for inference performance
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 presigned URL that you can use to connect to the MLflow UI attached to your tracking server; 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 private space or 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_hub_content_reference: Delete a hub content reference in order to remove a model from a private 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_mlflow_tracking_server: Deletes an MLflow Tracking Server
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_optimization_job: Deletes an optimization job
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 a node (also called a instance 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: Describes 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_mlflow_tracking_server: Returns information about an MLflow Tracking Server
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_optimization_job: Provides the properties of the specified optimization job
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_mlflow_tracking_servers: Lists all MLflow Tracking Servers
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_optimization_jobs: Lists the optimization jobs in your account and their properties
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_mlflow_tracking_server: Programmatically start an MLflow Tracking Server
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_mlflow_tracking_server: Programmatically stop an MLflow Tracking Server
stop_monitoring_schedule: Stops a previously started monitoring schedule
stop_notebook_instance: Terminates the ML compute instance
stop_optimization_job: Ends a running inference optimization job
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; 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_mlflow_tracking_server: Updates properties of an existing MLflow Tracking Server
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)