training_base_config: Export Airflow base training config from an estimator

In DyfanJones/sagemaker-r-workflow: sagemaker pipeline and workflows

Export Airflow base training config from an estimator

Description

Export Airflow base training config from an estimator

Usage

training_base_config(
  estimator,
  inputs = NULL,
  job_name = NULL,
  mini_batch_size = NULL
)

Arguments

estimator	(sagemaker.estimator.EstimatorBase): The estimator to export training config from. 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. * (dict[str, str] or dict[str, sagemaker.inputs.TrainingInput]) - If using multiple channels for training data, you can specify a dict 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.
job_name	(str): Specify a training job name if needed.
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.

Value

dict: Training config that can be directly used by SageMakerTrainingOperator in Airflow.

DyfanJones/sagemaker-r-workflow documentation built on April 3, 2022, 11:28 p.m.