SparkMLModel: SparkMLModel class

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

SparkMLModel class

Description

Model data and S3 location holder for MLeap serialized SparkML model. Calling :meth:'~sagemaker.model.Model.deploy' creates an Endpoint and return a Predictor to performs predictions against an MLeap serialized SparkML model .

Super classes

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

Methods

Public methods

• SparkMLModel$new()

• SparkMLModel$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()

---

Method new()

Initialize a SparkMLModel.

Usage

SparkMLModel$new(
  model_data,
  role = NULL,
  spark_version = "2.4",
  sagemaker_session = NULL,
  ...
)

Arguments

model_data

(str): The S3 location of a SageMaker model data “.tar.gz“ file. For SparkML, this will be the output that has been produced by the Spark job after serializing the Model via MLeap.

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.

spark_version

(str): Spark version you want to use for executing the inference (default: '2.4').

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. For local mode, please do not pass this variable.

...

: Additional parameters passed to the :class:'~sagemaker.model.Model' constructor.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

SparkMLModel$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.