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* add new tests * Try-catch errors from ray get * longer pbt run * Update pbt_example.py * Split trial and result and fix tests
Tune: Scalable Hyperparameter Search
====================================
Tune is a scalable framework for hyperparameter search with a focus on deep learning and deep reinforcement learning.
User documentation can be `found here <http://ray.readthedocs.io/en/latest/tune.html>`__.
Citing Tune
-----------
If Tune helps you in your academic research, you are encouraged to cite `our paper <https://arxiv.org/abs/1807.05118>`__. Here is an example bibtex:
.. code-block:: tex
@article{liaw2018tune,
title={Tune: A Research Platform for Distributed Model Selection and Training},
author={Liaw, Richard and Liang, Eric and Nishihara, Robert and
Moritz, Philipp and Gonzalez, Joseph E and Stoica, Ion},
journal={arXiv preprint arXiv:1807.05118},
year={2018}
}