KMeansModel: Reference KMeans s3 model data.

In DyfanJones/sagemaker-r-mlframework: sagemaker machine learning developed by amazon

Reference KMeans s3 model data.

Description

Calling :meth:'~sagemaker.model.Model.deploy' creates an Endpoint and return a Predictor to performs k-means cluster assignment.

Super classes

sagemaker.mlcore::ModelBase -> sagemaker.mlcore::Model -> KMeansModel

Methods

Public methods

• KMeansModel$new()

• KMeansModel$clone()

Inherited methods

• sagemaker.mlcore::ModelBase$format()

• sagemaker.mlcore::ModelBase$help()

• sagemaker.mlcore::Model$.create_sagemaker_model()

• sagemaker.mlcore::Model$check_neo_region()

• sagemaker.mlcore::Model$compile()

• sagemaker.mlcore::Model$delete_model()

• sagemaker.mlcore::Model$deploy()

• sagemaker.mlcore::Model$enable_network_isolation()

• sagemaker.mlcore::Model$package_for_edge()

• sagemaker.mlcore::Model$prepare_container_def()

• sagemaker.mlcore::Model$register()

• sagemaker.mlcore::Model$transformer()

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Method new()

Initialize KMeansPredictor Class

Usage

KMeansModel$new(model_data, role, sagemaker_session = NULL, ...)

Arguments

model_data

(str): The S3 location of a SageMaker model data “.tar.gz“ file.

role

(str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs that create Amazon SageMaker endpoints use this role to access training data and model artifacts. After the endpoint is created, the inference code might use the IAM role, if it needs to access an AWS resource.

sagemaker_session

(sagemaker.session.Session): Session object which manages interactions with Amazon SageMaker APIs and any other AWS services needed. If not specified, the estimator creates one using the default AWS configuration chain.

...

: Keyword arguments passed to the “FrameworkModel“ initializer.

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Method clone()

The objects of this class are cloneable with this method.

Usage

KMeansModel$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

DyfanJones/sagemaker-r-mlframework documentation built on March 18, 2022, 7:41 a.m.