PHS: A Toolbox for Parallel Hyperparameter Search
- We introduce an open source python framework named PHS-Parallel Hyperparameter Search to enable hyperparameter optimization on numerous compute instances of any arbitrary python function. This is achieved with minimal modifications inside the target function. Possible applications appear in expensive to evaluate numerical computations which strongly depend on hyperparameters such as machineWe introduce an open source python framework named PHS-Parallel Hyperparameter Search to enable hyperparameter optimization on numerous compute instances of any arbitrary python function. This is achieved with minimal modifications inside the target function. Possible applications appear in expensive to evaluate numerical computations which strongly depend on hyperparameters such as machine learning. Bayesian optimization is chosen as a sample efficient method to propose the next query set of parameters.…
| Author: | Peter Michael Habelitz, Janis KeuperORCiDGND |
|---|---|
| Year of Publication: | 2020 |
| Pagenumber: | 5 |
| Language: | English |
| Document Type: | Article (unreviewed) |
| Open Access: | Frei zugänglich |
| Institutes: | Bibliografie |
| Release Date: | 2021/01/05 |
| Licence (German): |  Es gilt das UrhG |
| ArXiv Id: | http://arxiv.org/abs/2002.11429 |