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Richard Liaw
62d0698097
This PR introduces the following changes: * Ray Tune -> Tune * [breaking] Creation of `schedulers/`, moving PBT, HyperBand into a submodule * [breaking] Search Algorithms now must take in experiment configurations via `add_configurations` rather through initialization * Support `"run": (function | class | str)` with automatic registering of trainable * Documentation Changes
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819 B
ReStructuredText
23 lines
819 B
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Tune: Scalable Hyperparameter Search
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====================================
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Tune is a scalable framework for hyperparameter search with a focus on deep learning and deep reinforcement learning.
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User documentation can be `found here <http://ray.readthedocs.io/en/latest/tune.html>`__.
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Citing Tune
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-----------
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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:
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.. code-block:: tex
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@article{liaw2018tune,
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title={Tune: A Research Platform for Distributed Model Selection and Training},
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author={Liaw, Richard and Liang, Eric and Nishihara, Robert and
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Moritz, Philipp and Gonzalez, Joseph E and Stoica, Ion},
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journal={arXiv preprint arXiv:1807.05118},
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year={2018}
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}
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