Topic 1 Question 337
A machine learning (ML) engineer is using Amazon SageMaker automatic model tuning (AMT) to optimize a model's hyperparameters. The ML engineer notices that the tuning jobs take a long time to run. The tuning jobs continue even when the jobs are not significantly improving against the objective metric.
The ML engineer needs the training jobs to optimize the hyperparameters more quickly.
How should the ML engineer configure the SageMaker AMT data types to meet these requirements?
Set Strategy to the Bayesian value.
Set RetryStrategy to a value of 1.
Set ParameterRanges to the narrow range Inferred from previous hyperparameter jobs.
Set TrainingJobEarlyStoppingType to the AUTO value.
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- 正解だと思う選択肢: D
Set TrainingJobEarlyStoppingType to the AUTO value
👍 2GS_772024/08/30 - 正解だと思う選択肢: A
Answer is A
👍 1aragon_saa2024/08/30 - 正解だと思う選択肢: D
If you are using the AWS SDK for Python (Boto3), set the TrainingJobEarlyStoppingType field of the HyperParameterTuningJobConfig object that you use to configure the tuning job to AUTO. https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-early-stopping.html
👍 1Tkhan12024/09/18
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