From 4ff6ca89f4d5e980d27c494c0b1e78d9a3835dd7 Mon Sep 17 00:00:00 2001
From: Richard Liaw <rliaw@berkeley.edu>
Date: Fri, 13 Dec 2019 10:38:17 -0800
Subject: [PATCH] [docs] slight doc modifications (#6466)

---
 doc/source/cluster-index.rst | 14 +++++++++++
 doc/source/index.rst         | 14 ++---------
 doc/source/tune.rst          | 47 +++---------------------------------
 doc/source/using-ray.rst     |  1 -
 4 files changed, 19 insertions(+), 57 deletions(-)
 create mode 100644 doc/source/cluster-index.rst

diff --git a/doc/source/cluster-index.rst b/doc/source/cluster-index.rst
new file mode 100644
index 000000000..bf60c016d
--- /dev/null
+++ b/doc/source/cluster-index.rst
@@ -0,0 +1,14 @@
+Deploying Ray
+=============
+
+How to setup your cluster and use Ray most effectively.
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Cluster Setup
+
+   autoscaling.rst
+   using-ray-on-a-cluster.rst
+   deploy-on-yarn.rst
+   deploy-on-kubernetes.rst
+   deploying-on-slurm.rst
diff --git a/doc/source/index.rst b/doc/source/index.rst
index 55d172674..051530930 100644
--- a/doc/source/index.rst
+++ b/doc/source/index.rst
@@ -159,7 +159,6 @@ Blog and Press
 
   - `Modern Parallel and Distributed Python: A Quick Tutorial on Ray <https://towardsdatascience.com/modern-parallel-and-distributed-python-a-quick-tutorial-on-ray-99f8d70369b8>`_
   - `Why Every Python Developer Will Love Ray <https://www.datanami.com/2019/11/05/why-every-python-developer-will-love-ray/>`_
-  - `Meet Ray, the Real-Time Machine-Learning Replacement for Spark <https://www.datanami.com/2017/03/28/meet-ray-real-time-machine-learning-replacement-spark/>`_
   - `Ray: A Distributed System for AI (BAIR) <http://bair.berkeley.edu/blog/2018/01/09/ray/>`_
   - `10x Faster Parallel Python Without Python Multiprocessing <https://towardsdatascience.com/10x-faster-parallel-python-without-python-multiprocessing-e5017c93cce1>`_
   - `Implementing A Parameter Server in 15 Lines of Python with Ray <https://ray-project.github.io/2018/07/15/parameter-server-in-fifteen-lines.html>`_
@@ -235,21 +234,12 @@ Getting Involved
    :caption: Ray Core
 
    using-ray.rst
+   configure.rst
+   cluster-index.rst
    Tutorials <https://github.com/ray-project/tutorial>
    Examples <auto_examples/overview.rst>
    package-ref.rst
 
-.. toctree::
-   :maxdepth: -1
-   :caption: Deploying Ray (Cluster Setup)
-
-   autoscaling.rst
-   using-ray-on-a-cluster.rst
-   deploy-on-yarn.rst
-   deploy-on-kubernetes.rst
-   deploying-on-slurm.rst
-
-
 .. toctree::
    :maxdepth: -1
    :caption: Tune
diff --git a/doc/source/tune.rst b/doc/source/tune.rst
index 85da4179e..d4e250912 100644
--- a/doc/source/tune.rst
+++ b/doc/source/tune.rst
@@ -67,49 +67,7 @@ If using TF2 and TensorBoard, Tune will also automatically generate TensorBoard
     :scale: 20%
     :align: center
 
-Distributed Quick Start
------------------------
-
-1. Import and initialize Ray by appending the following to your example script.
-
-.. code-block:: python
-
-    # Append to top of your script
-    import ray
-    import argparse
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--ray-address")
-    args = parser.parse_args()
-    ray.init(address=args.ray_address)
-
-Alternatively, download a full example script here: :download:`mnist_pytorch.py <../../python/ray/tune/examples/mnist_pytorch.py>`
-
-2. Download the following example Ray cluster configuration as ``tune-local-default.yaml`` and replace the appropriate fields:
-
-.. literalinclude:: ../../python/ray/tune/examples/tune-local-default.yaml
-   :language: yaml
-
-Alternatively, download it here: :download:`tune-local-default.yaml <../../python/ray/tune/examples/tune-local-default.yaml>`. See `Ray cluster docs here <autoscaling.html>`_.
-
-3. Run ``ray submit`` like the following.
-
-.. code-block:: bash
-
-    ray submit tune-local-default.yaml mnist_pytorch.py --args="--ray-address=localhost:6379" --start
-
-This will start Ray on all of your machines and run a distributed hyperparameter search across them.
-
-To summarize, here are the full set of commands:
-
-.. code-block:: bash
-
-    wget https://raw.githubusercontent.com/ray-project/ray/master/python/ray/tune/examples/mnist_pytorch.py
-    wget https://raw.githubusercontent.com/ray-project/ray/master/python/ray/tune/tune-local-default.yaml
-    ray submit tune-local-default.yaml mnist_pytorch.py --args="--ray-address=localhost:6379" --start
-
-
-Take a look at the `Distributed Experiments <tune-distributed.html>`_ documentation for more details, including:
+Take a look at the `Distributed Experiments <tune-distributed.html>`_ documentation for:
 
  1. Setting up distributed experiments on your local cluster
  2. Using AWS and GCP
@@ -124,7 +82,8 @@ Below are some blog posts and talks about Tune:
  - [blog] `Cutting edge hyperparameter tuning with Ray Tune <https://medium.com/riselab/cutting-edge-hyperparameter-tuning-with-ray-tune-be6c0447afdf>`_
  - [blog] `Simple hyperparameter and architecture search in tensorflow with Ray Tune <http://louiskirsch.com/ai/ray-tune>`_
  - [slides] `Talk given at RISECamp 2019 <https://docs.google.com/presentation/d/1v3IldXWrFNMK-vuONlSdEuM82fuGTrNUDuwtfx4axsQ/edit?usp=sharing>`_
- - [Talk] `Talk given at RISECamp 2018 <https://www.youtube.com/watch?v=38Yd_dXW51Q>`_
+ - [video] `Talk given at RISECamp 2018 <https://www.youtube.com/watch?v=38Yd_dXW51Q>`_
+ - [slides] `A Guide to Modern Hyperparameter Optimization (PyData LA 2019) <https://speakerdeck.com/richardliaw/a-modern-guide-to-hyperparameter-optimization>`_
 
 Open Source Projects using Tune
 -------------------------------
diff --git a/doc/source/using-ray.rst b/doc/source/using-ray.rst
index d4128e620..d4fcef20b 100644
--- a/doc/source/using-ray.rst
+++ b/doc/source/using-ray.rst
@@ -15,7 +15,6 @@ Finally, we've also included some content on using core Ray APIs with `Tensorflo
    using-ray-with-gpus.rst
    serialization.rst
    memory-management.rst
-   configure.rst
    troubleshooting.rst
    advanced.rst
    using-ray-with-tensorflow.rst