gp-parallel-ts

This is a python implementation of parallelised Bayesian optimisation via Thompson sampling. For more details, see our paper below.

Download

You can download the code from github

$ git clone https://github.com/kirthevasank/gp-parallel-ts

Installation & Getting Started

• Run source set_up_thompson to set up all environment variables.
• You also need to build the direct fortran library. For this cd into direct_fortran and run bash make_direct.sh. You will need a fortran compiler such as gnu95. Once this is done, you can run simple_direct_test.py to make sure that it was installed correctly.
• To test the installation, run bash run_all_tests.sh. Some of the tests are probabilistic and could fail at times. If this happens, run the same test several times and make sure it is not consistently failing.

Demo

• Check demo_simtime.py for a demo on how to run experiments with simulated time values (see paper below for experiments).
• We will also include a demo for real time experiments soon, but for now you can check out resnet_experiments/resnet_function_caller.py to see how to set it up. You need to use the RealWorkerManager class under bo/worker_manager.py

Some notes

• We choose the GP hyper-parameters every 25 iterations via marginal likelihood maximisation for each GP. The chosen values are printed out.
• We report progress on the optimisation every 20 iterations.

Citation

If you use any part of this code in your work, please cite our Arxiv paper:

@article{kandasamy2017asynchronous,
  title={Asynchronous Parallel Bayesian Optimisation via Thompson Sampling},
  author={Kandasamy, Kirthevasan and Krishnamurthy, Akshay and Schneider, Jeff and Poczos, Barnabas},
  journal={arXiv preprint arXiv:1705.09236},
  year={2017}
}

License

This software is released under the MIT license. For more details, please refer LICENSE.txt.

"Copyright 2017 Kirthevasan Kandasamy"

• For questions and bug reports please email [email protected]