Control of hyperparameter tuning based on machine learning

A computer-implemented method comprising:

• receiving, from a requesting device, a request to perform hyperparameter tuning of hyperparameters of an artificial intelligence (AI) model;
• dividing a hyperparameter search space into multiple search space portions;
• training, using machine learning, a generation model to determine whether a search space portion is likely to provide a set of hyperparameters that improves a success metric by which success of the hyperparameter tuning is evaluated;
• sequentially selecting at least a subset of the multiple search space portions, wherein for each search space portion that is selected, the processor is caused to:
• generating at least one set of hyperparameters from the search space portion;
• performing the hyperparameter tuning with the at least one set of hyperparameters as an input to determine whether the at least one set of hyperparameters improved the success metric;
• based at least on the determination of whether the at least one set of hyperparameters improved the success metric, applying the generation model to determine whether the search space portion is likely to provide another set of hyperparameters that improves the success metric;
• and
• ruling out the search space portion from providing further sets of hyperparameters in response to a determination that the search space portion is unlikely to provide another set of hyperparameters that improves the success metric;
• and
• terminating the performance of the hyperparameter tuning when all search space portions of the multiple search space portions are ruled out from providing further sets of hyperparameters.

An apparatus comprising a processor, and a storage communicatively coupled to the processor, and that stores instructions configured to cause the processor to:

• receive, from a requesting device, a request to perform hyperparameter tuning of hyperparameters of an artificial intelligence (AI) model;
• divide a hyperparameter search space into multiple search space portions;
• train, using machine learning, a generation model to determine whether a search space portion is likely to provide a set of hyperparameters that improves a success metric by which success of the hyperparameter tuning is evaluated;
• sequentially select at least a subset of the multiple search space portions, wherein for each search space portion that is selected, the processor is caused to:
• generate at least one set of hyperparameters from the search space portion;
• perform the hyperparameter tuning with the at least one set of hyperparameters as an input to determine whether the at least one set of hyperparameters improved the success metric;
• based at least on the determination of whether the at least one set of hyperparameters improved the success metric, apply the generation model to determine whether the search space portion is likely to provide another set of hyperparameters that improves the success metric;
• and
• rule out the search space portion from providing further sets of hyperparameters in response to a determination that the search space portion is unlikely to provide another set of hyperparameters that improves the success metric;
• and
• terminate the performance of the hyperparameter tuning when all search space portions of the multiple search space portions are ruled out from providing further sets of hyperparameters.