InferencePipeline: InferencePipeline for Sagemaker

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

Description

Creates a standard inference pipeline with the following steps in order:

• Train preprocessor

• Create preprocessor model

• Transform input data using preprocessor model

• Train estimator

• Create estimator model

• Endpoint configuration

• Deploy estimator model

Super class

stepfunctions::WorkflowTemplate -> InferencePipeline

Methods

Public methods

• InferencePipeline$new()

• InferencePipeline$build_workflow_definition()

• InferencePipeline$execute()

• InferencePipeline$clone()

Inherited methods

• stepfunctions::WorkflowTemplate$create()

• stepfunctions::WorkflowTemplate$format()

• stepfunctions::WorkflowTemplate$get_workflow()

• stepfunctions::WorkflowTemplate$render_graph()

---

Method new()

Initialize InferencePipeline class

Usage

InferencePipeline$new(
  preprocessor,
  estimator,
  inputs,
  s3_bucket,
  role,
  client = NULL,
  compression_type = NULL,
  content_type = NULL,
  pipeline_name = NULL
)

Arguments

preprocessor

(sagemaker.estimator.EstimatorBase): The estimator use to preprocess and transform the training data.

estimator

(sagemaker.estimator.EstimatorBase): The estimator to use for training. Can be a BYO estimator, Framework estimator or Amazon algorithm estimator.

inputs

: 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 'sagemaker.inputs.TrainingInput' objects.

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

• ('sagemaker.amazon.amazon_estimator.RecordSet') - A collection of Amazon '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 'sagemaker.amazon.amazon_estimator.RecordSet' objects, where each instance is a different channel of training data.

s3_bucket

(str): S3 bucket under which the output artifacts from the training job will be stored. The parent path used is built using the format: “s3://s3_bucket/pipeline_name/models/job_name/“. In this format, 'pipeline_name' refers to the keyword argument provided for TrainingPipeline. If a 'pipeline_name' argument was not provided, one is auto-generated by the pipeline as 'training-pipeline-<timestamp>'. Also, in the format, 'job_name' refers to the job name provided when calling the :meth:'TrainingPipeline.run()' method.

role

(str): An AWS IAM role (either name or full Amazon Resource Name (ARN)). This role is used to create, manage, and execute the Step Functions workflows.

client

(SFN.Client, optional): sfn client to use for creating and interacting with the training pipeline in Step Functions. (default: None)

compression_type

(str, optional): Compression type (Gzip/None) of the file for TransformJob. (default:None)

content_type

(str, optional): Content type (MIME) of the document to be used in preprocessing script. See SageMaker documentation for more details. (default:None)

pipeline_name

(str, optional): Name of the pipeline. This name will be used to name jobs (if not provided when calling execute()), models, endpoints, and S3 objects created by the pipeline. If a 'pipeline_name' argument was not provided, one is auto-generated by the pipeline as 'training-pipeline-<timestamp>'. (default:None)

---

Method build_workflow_definition()

Build the workflow definition for the inference pipeline with all the states involved.

Usage

InferencePipeline$build_workflow_definition()

Returns

:class:'~stepfunctions.steps.states.Chain': Workflow definition as a chain of states involved in the the inference pipeline.

---

Method execute()

Run the inference pipeline.

Usage

InferencePipeline$execute(job_name = NULL, hyperparameters = NULL)

Arguments

job_name

(str, optional): Name for the training job. This is also used as suffix for the preprocessing job as 'preprocess-<job_name>'. If one is not provided, a job name will be auto-generated. (default: None)

hyperparameters

(list, optional): Hyperparameters for the estimator training. (default: None)

Returns

:R:class:'~stepfunctions.workflow.Execution': Running instance of the inference pipeline.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

InferencePipeline$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.