DeepHyper¶
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): fully-automated search for high-performing deep neural network architectures
Hyperparameter Search for Deep Learning (Basic): optimizing hyperparameters for a given reference model
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.deephyper.search: 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.
Installation
Tutorials
User Guides
- Defining search problems
- Running locally
- Running on Theta (ALCF)
- Running on ThetaGPU (ALCF)
- Choosing & Scaling a Search
- Data Parallelism
- Managing Experiments with Balsam
- Configuring model execution with Balsam
- Neural Architecture Search (NAS)
- DeepHyper Analytics Tools
- Scikit-Learn Search
- Deep Ensemble
- Uncertainty Quantification