DeepHyper: Scalable Neural Architecture and Hyperparameter Search for Deep Neural Networks


DeepHyper is a scalable automated machine learning (AutoML) package for developing deep neural networks for scientific applications. It comprises two components:

  • Neural architecture search (NAS): It is designed for automatically searching for high-performing the deep neural network search_space.
  • Hyperparameter search (HPS): It is designed for automatically searching for high-performing hyperparameters for a given deep neural network search_space.

DeepHyper provides an infrastructure that targets experimental research in NAS and HPS methods, scalability, and portability across diverse supercomputers. It comprises three modules:

  • benchmark: Set of test problems for NAS and HPS that can be used for comparing different search methods. They can serve as examples to build new user-defined problems.
  • evaluator: Set of objects to run NAS and HPS on different target systems (from laptop to supercomputers) covering different use cases (quick/light experiments on laptop for testing and development to large production runs on supercomputers).
  • search: Set of search methods for NAS and HPS. It provides a modular way to define new search HPS and NAS search methods and submodules for implementing HPS and NAS.

DeepHyper installation requires Python 3.6.

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