[tune] Tune Facelift (#2472)

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

python/ray/tune/README.rst

@@ -1,28 +1,22 @@
-Ray.tune: Hyperparameter Optimization Framework
-===============================================
+Tune: Scalable Hyperparameter Search
+====================================
 
-Ray.tune is a hyperparameter tuning framework for long-running tasks such as RL and deep learning training.
+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>`__.
 
-Implementation overview
------------------------
 
-At a high level, Ray.tune takes in JSON experiment configs (e.g. that defines the grid or random search)
-and compiles them into a number of `Trial` objects. It schedules trials on the Ray cluster using a given
-`TrialScheduler` implementation (e.g. median stopping rule or HyperBand).
+Citing Tune
+-----------
 
-This is implemented as follows:
+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:
 
--  `variant_generator.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/suggest/variant_generator.py>`__
-   parses the config and generates the trial variants.
+.. code-block:: tex
 
--  `trial.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/trial.py>`__ manages the lifecycle
-   of the Ray actor responsible for executing the trial.
-
--  `trial_runner.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/tune.py>`__ tracks scheduling
-   state for all the trials of an experiment. TrialRunners are usually
-   created automatically by ``run_experiments(experiment_json)``, which parses and starts the experiments.
-
--  `trial_scheduler.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/trial_scheduler.py>`__
-   plugs into TrialRunner to implement custom prioritization or early stopping algorithms.
+    @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}
+    }