TuningStep: Sagemaker TuningStep task class

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

Description

Creates a Task State to execute a SageMaker HyperParameterTuning Job.

Super classes

stepfunctions::Block -> stepfunctions::State -> stepfunctions::Task -> TuningStep

Methods

Public methods

• TuningStep$new()

• TuningStep$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 TuningStep class

Usage

TuningStep$new(
  state_id,
  tuner,
  job_name,
  data,
  wait_for_completion = TRUE,
  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.

tuner

(sagemaker.tuner.HyperparameterTuner): The tuner to use in the TuningStep.

job_name

(str or Placeholder): Specify a tuning job name. We recommend to use :py:class:'~stepfunctions.inputs.ExecutionInput' placeholder collection to pass the value dynamically in each execution.

data

: Information about the training data. Please refer to the “fit()“ method of the associated estimator in the tuner, as this can take any of the following forms:

• (str) - The S3 location where training data is saved.

• (list[str, str] or list[str, sagemaker.inputs.TrainingInput]) - If using multiple channels for training data, you can specify a list mapping channel names to strings or :func:'~sagemaker.inputs.TrainingInput' objects.

• (sagemaker.inputs.TrainingInput) - Channel configuration for S3 data sources that can provide additional information about the training dataset. See :func:'sagemaker.inputs.TrainingInput' for full details.

• (sagemaker.amazon.amazon_estimator.RecordSet) - A collection of Amazon :class:'Record' objects serialized and stored in S3. For use with an estimator for an Amazon algorithm.

• (list[sagemaker.amazon.amazon_estimator.RecordSet]) - A list of :class:'sagemaker.amazon.amazon_estimator.RecordSet' objects, where each instance is a different channel of training data.

wait_for_completion

(bool, optional): Boolean value set to 'True' if the Task state should wait for the tuning job to complete before proceeding to the next step in the workflow. Set to 'False' if the Task state should submit the tuning job and proceed to the next step. (default: True)

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

TuningStep$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.