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.
- Create a new hyperparameter search problem
- Create a new neural architecture search problem
- How to manage your Balsam jobs?