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* make starting ray a separate page * concept * Apply suggestions from code review Co-Authored-By: Edward Oakes <ed.nmi.oakes@gmail.com> * more fics * Apply suggestions from code review Co-Authored-By: Edward Oakes <ed.nmi.oakes@gmail.com> Co-authored-by: Edward Oakes <ed.nmi.oakes@gmail.com>
82 lines
2.5 KiB
ReStructuredText
82 lines
2.5 KiB
ReStructuredText
.. _ref-cluster-setup:
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Manual Cluster Setup
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====================
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.. note::
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If you're using AWS or GCP you should use the automated `setup commands <autoscaling.html>`_.
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The instructions in this document work well for small clusters. For larger
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clusters, consider using the pssh package: ``sudo apt-get install pssh`` or
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the `setup commands for private clusters <autoscaling.html#quick-start-private-cluster>`_.
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Deploying Ray on a Cluster
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--------------------------
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This section assumes that you have a cluster running and that the nodes in the
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cluster can communicate with each other. It also assumes that Ray is installed
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on each machine. To install Ray, follow the `installation instructions`_.
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.. _`installation instructions`: http://ray.readthedocs.io/en/latest/installation.html
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Starting Ray on each machine
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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On the head node (just choose some node to be the head node), run the following.
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If the ``--redis-port`` argument is omitted, Ray will choose a port at random.
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.. code-block:: bash
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ray start --head --redis-port=6379
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The command will print out the address of the Redis server that was started
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(and some other address information).
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**Then on all of the other nodes**, run the following. Make sure to replace
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``<address>`` with the value printed by the command on the head node (it
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should look something like ``123.45.67.89:6379``).
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.. code-block:: bash
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ray start --address=<address>
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If you wish to specify that a machine has 10 CPUs and 1 GPU, you can do this
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with the flags ``--num-cpus=10`` and ``--num-gpus=1``. See the `Configuration <configure.html>`__ page for more information.
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Now we've started all of the Ray processes on each node Ray. This includes
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- Some worker processes on each machine.
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- An object store on each machine.
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- A raylet on each machine.
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- Multiple Redis servers (on the head node).
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To run some commands, start up Python on one of the nodes in the cluster, and do
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the following.
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.. code-block:: python
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import ray
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ray.init(address="<address>")
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Now you can define remote functions and execute tasks. For example, to verify
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that the correct number of nodes have joined the cluster, you can run the
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following.
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.. code-block:: python
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import time
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@ray.remote
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def f():
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time.sleep(0.01)
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return ray.services.get_node_ip_address()
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# Get a list of the IP addresses of the nodes that have joined the cluster.
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set(ray.get([f.remote() for _ in range(1000)]))
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Stopping Ray
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~~~~~~~~~~~~
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When you want to stop the Ray processes, run ``ray stop`` on each node.
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