ray/python/ray/tune

Tune: Scalable Hyperparameter Tuning
====================================

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://docs.ray.io/en/latest/tune.html>`__.


Tutorial
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To get started with Tune, try going through `our tutorial of using Tune with Keras <https://github.com/ray-project/tutorial/blob/master/tune_exercises/exercise_1_basics.ipynb>`__.

(Experimental): You can try out `the above tutorial on a free hosted server via Binder <https://mybinder.org/v2/gh/ray-project/tutorial/master?filepath=tune_exercises%2Fexercise_1_basics.ipynb>`__.


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}
    }