[Tune] Test

doc/source/conf.py

@@ -56,7 +56,7 @@
                 "ray.core.generated.TablePubsub",]
 for mod_name in MOCK_MODULES:
   sys.modules[mod_name] = mock.Mock()
-# ray.rllib.models.action_dist.py and 
+# ray.rllib.models.action_dist.py and
 # ray.rllib.models.lstm.py will use tf.VERSION
 sys.modules["tensorflow"].VERSION = "9.9.9"
 

doc/source/index.rst

@@ -40,11 +40,11 @@ View the `codebase on GitHub`_.
 
 Ray comes with libraries that accelerate deep learning and reinforcement learning development:
 
-- `Ray Tune`_: Hyperparameter Optimization Framework
-- `Ray RLlib`_: Scalable Reinforcement Learning
+- `Tune`_: Scalable Hyperparameter Search
+- `RLlib`_: Scalable Reinforcement Learning
 
-.. _`Ray Tune`: tune.html
-.. _`Ray RLlib`: rllib.html
+.. _`Tune`: tune.html
+.. _`RLlib`: rllib.html
 
 
 .. toctree::
@@ -67,12 +67,13 @@ Ray comes with libraries that accelerate deep learning and reinforcement learnin
 
 .. toctree::
    :maxdepth: 1
-   :caption: Ray Tune
+   :caption: Tune
 
    tune.rst
-   tune-config.rst
-   hyperband.rst
-   pbt.rst
+   tune-usage.rst
+   tune-schedulers.rst
+   tune-searchalg.rst
+   tune-package-ref.rst
 
 .. toctree::
    :maxdepth: 1

doc/source/rllib-training.rst

@@ -115,11 +115,12 @@ Here is an example of the basic usage:
            checkpoint = agent.save()
            print("checkpoint saved at", checkpoint)
 
+
 .. note::
 
     It's recommended that you run RLlib agents with `Tune <tune.html>`__, for easy experiment management and visualization of results. Just set ``"run": AGENT_NAME, "env": ENV_NAME`` in the experiment config.
 
-All RLlib agents are compatible with the `Tune API <tune.html#concepts>`__. This enables them to be easily used in experiments with `Tune <tune.html>`__. For example, the following code performs a simple hyperparam sweep of PPO:
+All RLlib agents are compatible with the `Tune API <tune-usage.html>`__. This enables them to be easily used in experiments with `Tune <tune.html>`__. For example, the following code performs a simple hyperparam sweep of PPO:
 
 .. code-block:: python
 

doc/source/tune-package-ref.rst

@@ -0,0 +1,33 @@
+Tune Package Reference
+=======================
+
+ray.tune
+--------
+
+.. automodule:: ray.tune
+    :members:
+    :exclude-members: TuneError, Trainable
+
+.. autoclass:: ray.tune.Trainable
+    :members:
+    :private-members:
+
+ray.tune.schedulers
+-------------------
+
+.. automodule:: ray.tune.schedulers
+    :members:
+    :show-inheritance:
+
+ray.tune.suggest
+----------------
+
+.. automodule:: ray.tune.suggest
+    :members:
+    :exclude-members: function, grid_search, SuggestionAlgorithm
+    :show-inheritance:
+
+.. autoclass:: ray.tune.suggest.SuggestionAlgorithm
+    :members:
+    :private-members:
+    :show-inheritance: