HyperSpace
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Machine learning (ML) models often contain numerous hyperparameters, free parameters that must be set before the models can be trained. As the number of model hyperparameters increases, their optimization becomes significantly more challenging as we face a combinatorial increase in potential model configurations. Similarly, there is an increased chance that our models’ hyperparameters interact in complex ways. HyperSpace allows a user to optimize complex machine learning algorithms based on their hyperparameters. HyperSpace works by parallelizing parameter search spaces, running Bayesian model based optimization (SMBO) over each of these spaces in parallel. It was designed to be as minimally invasive as possible such that very little change to existing code will be needed to get a user started.
- Site Accession Number:
- 8064
- Software Type:
- Scientific
- License(s):
- MIT License
- Programming Language(s):
- Python 3
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOEPrimary Award/Contract Number:AC05-00OR22725
- DOE Contract Number:
- AC05-00OR22725
- Code ID:
- 48745
- OSTI ID:
- code-48745
- Country of Origin:
- United States
Similar Records
HyperSpace: Distributed Bayesian Hyperparameter Optimization
HyperSpace: Distributed Bayesian Hyperparameter Optimization
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Sat Sep 01 00:00:00 EDT 2018
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HyperSpace: Distributed Bayesian Hyperparameter Optimization
Journal Article
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Fri Aug 31 20:00:00 EDT 2018
· Proceedings (Symposium on Computer Architecture and High Performance Computing)
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Software
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Wed Jan 30 19:00:00 EST 2019
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OSTI ID:code-48766