Tune Multiple Algorithms with Hyperparameter Optimization to Find the Best Model - Amazon SageMaker AI
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Tune Multiple Algorithms with Hyperparameter Optimization to Find the Best Model

To create a new hyperparameter optimization (HPO) job with Amazon SageMaker AI that tunes multiple algorithms, you must provide job settings that apply to all of the algorithms to be tested and a training definition for each of these algorithms. You must also specify the resources you want to use for the tuning job.

  • The job settings to configure include warm starting, early stopping, and the tuning strategy. Warm starting and early stopping are available only when tuning a single algorithm.

  • The training job definition to specify the name, algorithm source, objective metric, and the range of values, when required, to configure the set of hyperparameter values for each training job. It configures the channels for data inputs, data output locations, and any checkpoint storage locations for each training job. The definition also configures the resources to deploy for each training job, including instance types and counts, managed spot training, and stopping conditions.

  • The tuning job resources: to deploy, including the maximum number of concurrent training jobs that a hyperparameter tuning job can run concurrently and the maximum number of training jobs that the hyperparameter tuning job can run.

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You can create a new hyperparameter tuning job, clone a job, add, or edit tags to a job from the console. You can also use the search feature to find jobs by their name, creation time, or status. Alternatively, you can also hyperparameter tuning jobs with the SageMaker AI API.

  • In the console: To create a new job, open the Amazon SageMaker AI console at https://console.aws.amazon.com/sagemaker/, choose Hyperparameter tuning jobs from the Training, menu, and then choose Create hyperparameter tuning job. Then following the configuration steps to create a training job for each algorithm that you want to use. These steps are documented in the Create a Hyperparameter Optimization Tuning Job for One or More Algorithms (Console) topic.

    Note

    When you start the configuration steps, note that the warm start and early stopping features are not available to use with multi-algorithm HPO. If you want to use these features, you can only tune a single algorithm at a time.

  • With the API: For instructions on using the SageMaker API to create a hyperparameter tuning job, see Example: Hyperparameter Tuning Job. When you call CreateHyperParameterTuningJob to tune multiple algorithms, you must provide a list of training definitions using TrainingJobDefinitions instead of specifying a single TrainingJobDefinition. You must provide job settings that apply to all of the algorithms to be tested and a training definition for each of these algorithms. You must also specify the resources that you want to use for the tuning job. Choose only one of these definition types depending on the number of algorithms that are being tuned.

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