chore: import upstream snapshot with attribution

python/ray/tune/README.rst

@@ -0,0 +1,30 @@
+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}
+    }