tuning_config: Export Airflow tuning config from a HyperparameterTuner

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

Export Airflow tuning config from a HyperparameterTuner

Description

Export Airflow tuning config from a HyperparameterTuner

Usage

tuning_config(
  tuner,
  inputs,
  job_name = NULL,
  include_cls_metadata = FALSE,
  mini_batch_size = NULL
)

Arguments

tuner	(sagemaker.tuner.HyperparameterTuner): The tuner to export tuning config from.
inputs	: 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. * (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. * (dict[str, one the forms above]): Required by only tuners created via the factory method “HyperparameterTuner.create()“. The keys should be the same estimator names as keys for the “estimator_list“ argument of the “HyperparameterTuner.create()“ method.
job_name	(str): Specify a tuning job name if needed.
include_cls_metadata	: It can take one of the following two forms. * (bool) - Whether or not the hyperparameter tuning job should include information about the estimator class (default: False). This information is passed as a hyperparameter, so if the algorithm you are using cannot handle unknown hyperparameters (e.g. an Amazon SageMaker built-in algorithm that does not have a custom estimator in the Python SDK), then set “include_cls_metadata“ to “False“. * (dict[str, bool]) - This version should be used for tuners created via the factory method “HyperparameterTuner.create()“, to specify the flag for individual estimators provided in the “estimator_list“ argument of the method. The keys would be the same estimator names as in “estimator_list“. If one estimator doesn't need the flag set, then no need to include it in the dictionary. If none of the estimators need the flag set, then an empty dictionary ““ must be used.
mini_batch_size	: It can take one of the following two forms. * (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. * (dict[str, int]) - This version should be used for tuners created via the factory method “HyperparameterTuner.create()“, to specify the value for individual estimators provided in the “estimator_list“ argument of the method. The keys would be the same estimator names as in “estimator_list“. If one estimator doesn't need the value set, then no need to include it in the dictionary. If none of the estimators need the value set, then an empty dictionary ““ must be used.

Value

list: Tuning config that can be directly used by SageMakerTuningOperator in Airflow.

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