ray/doc/source/ray-core/starting-ray.rst

Starting Ray
============

This page covers how to start Ray on your single machine or cluster of machines.

.. tip:: Be sure to have :ref:`installed Ray <installation>` before following the instructions on this page.


What is the Ray runtime?
------------------------

Ray programs are able to parallelize and distribute by leveraging an underlying *Ray runtime*.
The Ray runtime consists of multiple services/processes started in the background for communication, data transfer, scheduling, and more. The Ray runtime can be started on a laptop, a single server, or multiple servers.

There are three ways of starting the Ray runtime:

* Implicitly via ``ray.init()`` (:ref:`start-ray-init`)
* Explicitly via CLI (:ref:`start-ray-cli`)
* Explicitly via the cluster launcher (:ref:`start-ray-up`)

.. _start-ray-init:

Starting Ray on a single machine
--------------------------------

Calling ``ray.init()`` (without any ``address`` args) starts a Ray runtime on your laptop/machine. This laptop/machine becomes the  "head node".

.. note::

  In recent versions of Ray (>=1.5), ``ray.init()`` will automatically be called on the first use of a Ray remote API.

.. tabbed:: Python

    .. code-block:: python

        import ray
        # Other Ray APIs will not work until `ray.init()` is called.
        ray.init()

.. tabbed:: Java

    .. code-block:: java

        import io.ray.api.Ray;

        public class MyRayApp {

          public static void main(String[] args) {
            // Other Ray APIs will not work until `Ray.init()` is called.
            Ray.init();
            ...
          }
        }

.. tabbed:: C++

    .. code-block:: c++

        #include <ray/api.h>
        // Other Ray APIs will not work until `ray::Init()` is called.
        ray::Init()

When the process calling ``ray.init()`` terminates, the Ray runtime will also terminate. To explicitly stop or restart Ray, use the shutdown API.

.. tabbed:: Python

    .. code-block:: python

        import ray
        ray.init()
        ... # ray program
        ray.shutdown()

.. tabbed:: Java

    .. code-block:: java

        import io.ray.api.Ray;

        public class MyRayApp {

          public static void main(String[] args) {
            Ray.init();
            ... // ray program
            Ray.shutdown();
          }
        }

.. tabbed:: C++

    .. code-block:: c++

        #include <ray/api.h>
        ray::Init()
        ... // ray program
        ray::Shutdown()

To check if Ray is initialized, use the ``is_initialized`` API.

.. tabbed:: Python

    .. code-block:: python

        import ray
        ray.init()
        assert ray.is_initialized()

        ray.shutdown()
        assert not ray.is_initialized()

.. tabbed:: Java

    .. code-block:: java

        import io.ray.api.Ray;

        public class MyRayApp {

        public static void main(String[] args) {
                Ray.init();
                Assert.assertTrue(Ray.isInitialized());
                Ray.shutdown();
                Assert.assertFalse(Ray.isInitialized());
            }
        }

.. tabbed:: C++

    .. code-block:: c++

        #include <ray/api.h>

        int main(int argc, char **argv) {
            ray::Init();
            assert(ray::IsInitialized());

            ray::Shutdown();
            assert(!ray::IsInitialized());
        }

See the `Configuration <configure.html>`__ documentation for the various ways to configure Ray.

.. _start-ray-cli:

Starting Ray via the CLI (``ray start``)
----------------------------------------

Use ``ray start`` from the CLI to start a 1 node ray runtime on a machine. This machine becomes the "head node".

.. code-block:: bash

  $ ray start --head --port=6379

  Local node IP: 192.123.1.123
  2020-09-20 10:38:54,193 INFO services.py:1166 -- View the Ray dashboard at http://localhost:8265

  --------------------
  Ray runtime started.
  --------------------

  ...


You can connect to this Ray runtime by starting a driver process on the same node as where you ran ``ray start``:

.. tabbed:: Python

  .. code-block:: python

    # This must
    import ray
    ray.init(address='auto')

.. tabbed:: java

    .. code-block:: java

      import io.ray.api.Ray;

      public class MyRayApp {

        public static void main(String[] args) {
          Ray.init();
          ...
        }
      }

    .. code-block:: bash

      java -classpath <classpath> \
        -Dray.address=<address> \
        <classname> <args>

.. tabbed:: C++

    .. code-block:: c++

      #include <ray/api.h>

      int main(int argc, char **argv) {
        ray::Init();
        ...
      }

    .. code-block:: bash

      RAY_ADDRESS=<address> ./<binary> <args>


You can connect other nodes to the head node, creating a Ray cluster by also calling ``ray start`` on those nodes. See :ref:`manual-cluster` for more details. Calling ``ray.init(address="auto")`` on any of the cluster machines will connect to the ray cluster.

.. _start-ray-up:

Launching a Ray cluster (``ray up``)
------------------------------------

Ray clusters can be launched with the :ref:`Cluster Launcher <cluster-cloud>`.
The ``ray up`` command uses the Ray cluster launcher to start a cluster on the cloud, creating a designated "head node" and worker nodes. Underneath the hood, it automatically calls ``ray start`` to create a Ray cluster.

Your code **only** needs to execute on one machine in the cluster (usually the head node). Read more about :ref:`running programs on a Ray cluster <using-ray-on-a-cluster>`.

To connect to the existing cluster, similar to the method outlined in :ref:`start-ray-cli`, you must call ``ray.init`` and specify the address of the Ray cluster when initializing Ray in your code. This allows your script to connect to the existing Ray runtime on the cluster.

.. code-block:: python

    ray.init(address="auto")

Note that the machine calling ``ray up`` will not be considered as part of the Ray cluster, and therefore calling ``ray.init`` on that same machine will not attach to the cluster.

What's next?
------------

Check out our `Deployment section <cluster/index.html>`_ for more information on deploying Ray in different settings, including Kubernetes, YARN, and SLURM.