ProcessingStep: Sagemaker ProcessingStep 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 Processing Job.

Super classes

stepfunctions::Block -> stepfunctions::State -> stepfunctions::Task -> ProcessingStep

Methods

Public methods

• ProcessingStep$new()

• ProcessingStep$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 ProcessingStep class

Usage

ProcessingStep$new(
  state_id,
  processor,
  job_name,
  inputs = NULL,
  outputs = NULL,
  experiment_config = NULL,
  container_arguments = NULL,
  container_entrypoint = NULL,
  kms_key_id = 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.

processor

(sagemaker.processing.Processor): The processor for the processing step.

job_name

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

inputs

(list[:class:'~sagemaker.processing.ProcessingInput']): Input files for the processing job. These must be provided as :class:'~sagemaker.processing.ProcessingInput' objects (default: None).

outputs

(list[:class:'~sagemaker.processing.ProcessingOutput']): Outputs for the processing job. These can be specified as either path strings or :class:'~sagemaker.processing.ProcessingOutput' objects (default: None).

experiment_config

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

container_arguments

([str]): The arguments for a container used to run a processing job.

container_entrypoint

([str]): The entrypoint for a container used to run a processing job.

kms_key_id

(str): The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the processing job output. KmsKeyId can be an ID of a KMS key, ARN of a KMS key, alias of a KMS key, or alias of a KMS key. The KmsKeyId is applied to all outputs.

wait_for_completion

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

ProcessingStep$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.