ray/doc/source/using-ray-on-a-cluster.rst

.. _ref-cluster-setup:

Manual Cluster Setup
====================

.. note::

    If you're using AWS, Azure or GCP you should use the automated `setup commands <autoscaling.html>`_.

The instructions in this document work well for small clusters. For larger
clusters, consider using the pssh package: ``sudo apt-get install pssh`` or
the `setup commands for private clusters <autoscaling.html#quick-start-private-cluster>`_.


Deploying Ray on a Cluster
--------------------------

This section assumes that you have a cluster running and that the nodes in the
cluster can communicate with each other. It also assumes that Ray is installed
on each machine. To install Ray, follow the `installation instructions`_.

.. _`installation instructions`: http://docs.ray.io/en/latest/installation.html

Starting Ray on each machine
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

On the head node (just choose some node to be the head node), run the following.
If the ``--redis-port`` argument is omitted, Ray will choose a port at random.

.. code-block:: bash

  ray start --head --redis-port=6379

The command will print out the address of the Redis server that was started
(and some other address information).

**Then on all of the other nodes**, run the following. Make sure to replace
``<address>`` with the value printed by the command on the head node (it
should look something like ``123.45.67.89:6379``).

.. code-block:: bash

  ray start --address=<address>

If you wish to specify that a machine has 10 CPUs and 1 GPU, you can do this
with the flags ``--num-cpus=10`` and ``--num-gpus=1``. See the `Configuration <configure.html>`__ page for more information.

Now we've started all of the Ray processes on each node Ray. This includes

- Some worker processes on each machine.
- An object store on each machine.
- A raylet on each machine.
- Multiple Redis servers (on the head node).

To run some commands, start up Python on one of the nodes in the cluster, and do
the following.

.. code-block:: python

  import ray
  ray.init(address="<address>")

Now you can define remote functions and execute tasks. For example, to verify
that the correct number of nodes have joined the cluster, you can run the
following.

.. code-block:: python

  import time

  @ray.remote
  def f():
      time.sleep(0.01)
      return ray.services.get_node_ip_address()

  # Get a list of the IP addresses of the nodes that have joined the cluster.
  set(ray.get([f.remote() for _ in range(1000)]))

Stopping Ray
~~~~~~~~~~~~

When you want to stop the Ray processes, run ``ray stop`` on each node.