Files
ray/python/ray/tune
Jimpachnet d3551dd8df [tune] Added possibility to execute infinite recovery retries for a trial (#3901)
Allows to let a trial try to do infinite recoveries by setting _max_failures_ to a negative number.
2019-01-31 02:21:16 -08:00
..
2019-01-30 21:01:12 -08:00
2019-01-15 10:37:28 -08:00
2019-01-15 10:37:28 -08:00

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>`__.


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/Tune.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%2FTune.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}
    }