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/master/tune.html>__.

Tutorial

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}
}