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PARyOpt: A software for Parallel Asynchronous Remote Bayesian Optimization
  • Balaji Sesha Sarath Pokuri, Iowa State University
  • Alec Lofquist, Iowa State University
  • Chad M. Risko, University of Kentucky
  • Baskar Ganapathysubramanian, Iowa State University
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PARyOpt is a python based implementation of the Bayesian optimization routine designed for remote and asynchronous function evaluations. Bayesian optimization is especially attractive for computational optimization due to its low cost function footprint as well as the ability to account for uncertainties in data. A key challenge to efficiently deploy any optimization strategy on distributed computing systems is the synchronization step, where data from multiple function calls is assimilated to identify the next campaign of function calls. Bayesian optimization provides an elegant approach to overcome this issue via asynchronous updates. We formulate, develop and implement a parallel, asynchronous variant of Bayesian optimization. The framework is robust and resilient to external failures. We show how such asynchronous evaluations help reduce the total optimization wall clock time for a suite of test problems. Additionally, we show how the software design of the framework allows easy extension to response surface reconstruction (Kriging), providing a high performance software for autonomous exploration. The software is available on PyPI, with examples and documentation.


This is a pre-print of the article Pokuri, Balaji Sesha Sarath, Alec Lofquist, Chad M. Risko, and Baskar Ganapathysubramanian. "PARyOpt: A software for Parallel Asynchronous Remote Bayesian Optimization." arXiv preprint arXiv:1809.04668 (2018). Posted with permission.

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Balaji Sesha Sarath Pokuri, Alec Lofquist, Chad M. Risko and Baskar Ganapathysubramanian. "PARyOpt: A software for Parallel Asynchronous Remote Bayesian Optimization" arXiv (2018)
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