ModelStep: Sagemaker ModelStep task class

In DyfanJones/aws-step-functions-data-science-sdk-r: R SDK for `AWS Stepfunctions`

Description

Creates a Task State to 'create a model in SageMaker' https://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateModel.html.

Super classes

stepfunctions::Block -> stepfunctions::State -> stepfunctions::Task -> ModelStep

Methods

Public methods

• ModelStep$new()

• ModelStep$clone()

Inherited methods

• stepfunctions::Block$is_field_allowed()

• stepfunctions::Block$to_json()

• stepfunctions::State$.next()

• stepfunctions::State$accept()

• stepfunctions::State$add_catch()

• stepfunctions::State$add_retry()

• stepfunctions::State$format()

• stepfunctions::State$output()

• stepfunctions::State$to_list()

• stepfunctions::State$update_parameters()

• stepfunctions::Task$allowed_fields()

---

Method new()

Initialize ModelStep class

Usage

ModelStep$new(
  state_id,
  model,
  model_name = NULL,
  instance_type = NULL,
  tags = NULL,
  ...
)

Arguments

state_id

(str): State name whose length **must be** less than or equal to 128 unicode characters. State names **must be** unique within the scope of the whole state machine.

model

(sagemaker.model.Model): The SageMaker model to use in the ModelStep. If :py:class:'TrainingStep' was used to train the model and saving the model is the next step in the workflow, the output of :py:func: 'TrainingStep.get_expected_model()' can be passed here.

model_name

(str or Placeholder, optional): Specify a model name, this is required for creating the model. We recommend to use :py:class: '~stepfunctions.inputs.ExecutionInput' placeholder collection to pass the value dynamically in each execution.

instance_type

(str, optional): The EC2 instance type to deploy this Model to. For example, 'ml.p2.xlarge'.

tags

(list[list], optional): 'List to tags' https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html to associate with the resource.

...

: Extra Fields passed to Task class

---

Method clone()

The objects of this class are cloneable with this method.

Usage

ModelStep$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

DyfanJones/aws-step-functions-data-science-sdk-r documentation built on Dec. 17, 2021, 5:31 p.m.