sagemaker: Amazon SageMaker Service
In paws: Amazon Web Services Software Development Kit
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
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_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
create_presigned_mlflow_tracking_server_url Returns a presigned URL that you can use to connect to the MLflow UI attached to your tracking server
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 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
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_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
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_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
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_optimization_job Provides the properties of the specified optimization job
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_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
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_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
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_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
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_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
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)
paws documentation built on Sept. 17, 2024, 5:07 p.m.
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| 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 |
Optional credentials shorthand for the config parameter
|
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 |
| 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_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 |
| create_presigned_mlflow_tracking_server_url | Returns a presigned URL that you can use to connect to the MLflow UI attached to your tracking server |
| 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 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 |
| 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_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 |
| 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_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 |
| 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_optimization_job | Provides the properties of the specified optimization job |
| 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_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 |
| 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_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 |
| 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_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 |
| 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_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 |
| 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)
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