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:

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

  • deephyper.benchmark: Tools for defining NAS and HPS problems, as well as a curated set of sample benchmark problems for judging the efficacy of novel search algorithms.

  • deephyper.evaluator : A simple interface for NAS and HPS codes to dispatch model evaluation tasks. Implementations range from subprocess for laptop experiments to ray and balsam for large-scale runs on HPC systems.

  • Search methods for NAS and HPS. By extending the generic Search class, one can easily add new NAS or HPS methods to DeepHyper.

DeepHyper installation requires Python 3.7.

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