[Cluster Launcher] Re Org the cluster launcher pages.

doc/source/cluster/cloud.rst

@@ -1,7 +1,7 @@
 .. _cluster-cloud:
 
-Launching clusters on the cloud
-===============================
+Launching Cloud Clusters
+========================
 
 This section provides instructions for configuring the Ray Cluster Launcher to use with AWS/Azure/GCP, an existing Kubernetes cluster, or on a private cluster of host machines.
 
@@ -228,3 +228,128 @@ Security
 
 On cloud providers, nodes will be launched into their own security group by default, with traffic allowed only between nodes in the same group. A new SSH key will also be created and saved to your local machine for access to the cluster.
 
+.. _cluster-config:
+
+Configuring your Cluster
+------------------------
+
+The Ray Cluster Launcher requires a *cluster configuration file*, which specifies some important details about the cluster. At a minimum, we need to specify:
+
+ * the name of your cluster,
+ * the number of workers in the cluster
+ * the cloud provider
+ * any setup commands that should run on the node upon launch.
+
+Here is an example cluster configuration file:
+
+.. code-block:: yaml
+
+    # A unique identifier for this cluster.
+    cluster_name: basic-ray
+
+    # The maximum number of workers nodes to launch in addition to the head
+    # node.
+    max_workers: 0 # this means zero workers
+
+    # Cloud-provider specific configuration.
+    provider:
+       type: aws
+       region: us-west-2
+       availability_zone: us-west-2a
+
+    # How Ray will authenticate with newly launched nodes.
+    auth:
+       ssh_user: ubuntu
+
+    setup_commands:
+      - pip install ray[all]
+      # The following line demonstrate that you can specify arbitrary
+      # startup scripts on the cluster.
+      - touch /tmp/some_file.txt
+
+Most of the example YAML file is optional. Here is a `reference minimal YAML file <https://github.com/ray-project/ray/tree/master/python/ray/autoscaler/aws/example-minimal.yaml>`__, and you can find the defaults for `optional fields in this YAML file <https://github.com/ray-project/ray/tree/master/python/ray/autoscaler/aws/example-full.yaml>`__.
+
+You are encouraged to copy the example YAML file and modify it to your needs. This may include adding additional setup commands to install libraries or sync local data files.
+
+Setup Commands
+~~~~~~~~~~~~~~
+
+.. note:: After you have customized the nodes, it is also a good idea to create a new machine image (or docker container) and use that in the config file. This reduces worker setup time, improving the efficiency of auto-scaling.
+
+The setup commands you use should ideally be *idempotent*, that is, can be run more than once. This allows Ray to update nodes after they have been created. You can usually make commands idempotent with small modifications, e.g. ``git clone foo`` can be rewritten as ``test -e foo || git clone foo`` which checks if the repo is already cloned first.
+
+
+Common cluster configurations
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The ``example-full.yaml`` configuration is enough to get started with Ray, but for more compute intensive workloads you will want to change the instance types to e.g. use GPU or larger compute instance by editing the yaml file. Here are a few common configurations:
+
+**GPU single node**: use Ray on a single large GPU instance.
+
+.. code-block:: yaml
+
+    max_workers: 0
+    head_node:
+        InstanceType: p2.8xlarge
+
+**Docker**: Specify docker image. This executes all commands on all nodes in the docker container,
+and opens all the necessary ports to support the Ray cluster.
+
+.. code-block:: yaml
+
+    docker:
+        image: tensorflow/tensorflow:1.5.0-py3
+        container_name: ray_docker
+
+If Docker is not installed, add the following commands to ``initialization_commands`` to install it.
+
+.. code-block:: yaml
+
+    initialization_commands:
+    - curl -fsSL https://get.docker.com -o get-docker.sh
+    - sudo sh get-docker.sh
+    - sudo usermod -aG docker $USER
+    - sudo systemctl restart docker -f
+
+
+**Mixed GPU and CPU nodes**: for RL applications that require proportionally more
+CPU than GPU resources, you can use additional CPU workers with a GPU head node.
+
+.. code-block:: yaml
+
+    max_workers: 10
+    head_node:
+        InstanceType: p2.8xlarge
+    worker_nodes:
+        InstanceType: m4.16xlarge
+
+**Autoscaling CPU cluster**: use a small head node and have Ray auto-scale
+workers as needed. This can be a cost-efficient configuration for clusters with
+bursty workloads. You can also request spot workers for additional cost savings.
+
+.. code-block:: yaml
+
+    min_workers: 0
+    max_workers: 10
+    head_node:
+        InstanceType: m4.large
+    worker_nodes:
+        InstanceMarketOptions:
+            MarketType: spot
+        InstanceType: m4.16xlarge
+
+**Autoscaling GPU cluster**: similar to the autoscaling CPU cluster, but
+with GPU worker nodes instead.
+
+.. code-block:: yaml
+
+    min_workers: 0  # NOTE: older Ray versions may need 1+ GPU workers (#2106)
+    max_workers: 10
+    head_node:
+        InstanceType: m4.large
+    worker_nodes:
+        InstanceMarketOptions:
+            MarketType: spot
+        InstanceType: p2.xlarge
+
+

doc/source/cluster/index.rst

@@ -1,9 +1,9 @@
 .. _cluster-index:
 
-Multi-node Ray Overview
-=======================
+Distributed Ray Overview
+========================
 
-One of Ray's strengths is the ability to leverage multiple machines in the same program. You can do this by creating and connecting to a Ray cluster.
+One of Ray's strengths is the ability to leverage multiple machines in the same program Ray can, of course, be run on a single machine (and is done so often) but the real power is using Ray on a cluster of machines.
 
 Key Concepts
 ------------