TrainingStep: Sagemaker TrainingStep 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 Training Job' https://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateTrainingJob.html The TrainingStep will also create a model by default, and the model shares the same name as the training job.

Super classes

stepfunctions::Block -> stepfunctions::State -> stepfunctions::Task -> TrainingStep

Methods

Public methods

• TrainingStep$new()

• TrainingStep$get_expected_model()

• TrainingStep$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 TrainingStep class

Usage

TrainingStep$new(
  state_id,
  estimator,
  job_name,
  data = NULL,
  hyperparameters = NULL,
  mini_batch_size = NULL,
  experiment_config = NULL,
  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.

estimator

(sagemaker.estimator.EstimatorBase): The estimator for the training step. Can be a 'BYO estimator, Framework estimator' https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html or 'Amazon built-in algorithm estimator' https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html.

job_name

(str or Placeholder): Specify a training job name, this is required for the training job to run. 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, 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.

hyperparameters

(list, optional): Specify the hyper parameters for the training. (Default: None)

mini_batch_size

(int): Specify this argument only when estimator is a built-in estimator of an Amazon algorithm. For other estimators, batch size should be specified in the estimator.

experiment_config

(list, optional): Specify the experiment config for the training. (Default: None)

wait_for_completion

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

Build Sagemaker model representation of the expected trained model from the Training step. This can be passed to the ModelStep to save the trained model in Sagemaker.

Usage

TrainingStep$get_expected_model(model_name = NULL)

Arguments

model_name

(str, optional): Specify a model name. If not provided, training job name will be used as the model name.

Returns

sagemaker.model.Model: Sagemaker model representation of the expected trained model.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

TrainingStep$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.