[docs] Hotfix for removing unneeded files (#5383)

doc/source/examples.rst

@@ -1,14 +0,0 @@
-Examples
-========
-
-MapReduce
----------
-
-Parameter Server
-----------------
-
-Deep Learning
--------------
-
-Asynchronous Advantage Actor-Critic (A3C)
------------------------------------------

doc/source/index.rst

@@ -70,27 +70,27 @@ Tune Quick Start
 
 `Tune`_ is a scalable framework for hyperparameter search built on top of Ray with a focus on deep learning and deep reinforcement learning.
 
-.. code-block:: python
+.. note::
 
-   import torch.optim as optim
-   from ray import tune
-   from ray.tune.examples.mnist_pytorch import get_data_loaders, Net, train, test
+    To run this example, you will need to install the following:
 
-   def train_mnist(config):
-       train_loader, test_loader = get_data_loaders()
-       model = Net(config)
-       optimizer = optim.SGD(model.parameters(), lr=config["lr"])
-       for i in range(10):
-           train(model, optimizer, train_loader)
-           acc = test(model, test_loader)
-           tune.track.log(mean_accuracy=acc)
+    .. code-block:: bash
 
-   analysis = tune.run(
-       train_mnist,
-       stop={"mean_accuracy": 0.98},
-       config={"lr": tune.grid_search([0.001, 0.01, 0.1])})
+        $ pip install ray torch torchvision filelock
 
-   print("Best config: ", analysis.get_best_config())
+
+This example runs a small grid search to train a CNN using PyTorch and Tune.
+
+.. literalinclude:: ../../python/ray/tune/tests/example.py
+   :language: python
+   :start-after: __quick_start_begin__
+   :end-before: __quick_start_end__
+
+If TensorBoard is installed, automatically visualize all trial results:
+
+.. code-block:: bash
+
+    tensorboard --logdir ~/ray_results
 
 .. _`Tune`: tune.html
 
@@ -171,7 +171,6 @@ The following are good places to discuss Ray.
    advanced.rst
    troubleshooting.rst
    package-ref.rst
-   examples.rst
 
 .. toctree::
    :maxdepth: 1

doc/source/inspect.rst

@@ -40,7 +40,7 @@ To get information about the current nodes in your cluster, you can use ``ray.no
     :noindex:
 
 
-.. code-block:: ipython3
+.. code-block:: python
 
     >>> import ray
     >>> ray.init()

rllib_doc/index.rst

@@ -1,17 +0,0 @@
-Ray
-===
-
-.. toctree::
-   :maxdepth: 1
-   :caption: RLlib
-
-   rllib.rst
-   rllib-training.rst
-   rllib-env.rst
-   rllib-models.rst
-   rllib-algorithms.rst
-   rllib-offline.rst
-   rllib-concepts.rst
-   rllib-examples.rst
-   rllib-dev.rst
-   rllib-package-ref.rst