To run this demo, you will need:

* Python installed on your development machine (typically your laptop), and
* an account at your preferred cloud provider (AWS, GCP, Azure, Aliyun, or vSphere).

Setup
~~~~~

Before we start, you will need to install some Python dependencies as follows:

.. tab-set::

   .. tab-item:: Ray Team Supported
      :sync: Ray Team Supported

      .. tab-set::

         .. tab-item:: AWS
            :sync: AWS

            .. code-block:: shell

                $ pip install -U "ray[default]" boto3

         .. tab-item:: Azure
            :sync: Azure

            .. code-block:: shell

                $ pip install -U "ray[default]" azure-cli azure-core

         .. tab-item:: GCP
            :sync: GCP

            .. code-block:: shell

                $ pip install -U "ray[default]" google-api-python-client

   .. tab-item:: Community Supported
      :sync: Community Supported

      .. tab-set::

         .. tab-item:: Aliyun
            :sync: Aliyun

            .. code-block:: shell

                $ pip install -U "ray[default]" aliyun-python-sdk-core aliyun-python-sdk-ecs

            Aliyun Cluster Launcher Maintainers (GitHub handles): @zhuangzhuang131419, @chenk008

         .. tab-item:: vSphere
            :sync: vSphere

            .. code-block:: shell

                $ pip install -U "ray[default]"

            vSphere Cluster Launcher Maintainers (GitHub handles): @roshankathawate, @ankitasonawane30, @VamshikShetty


Next, if you're not set up to use your cloud provider from the command line, you'll have to configure your credentials:

.. tab-set::

   .. tab-item:: Ray Team Supported
      :sync: Ray Team Supported

      .. tab-set::

         .. tab-item:: AWS
            :sync: AWS

            Configure your credentials in ``~/.aws/credentials`` as described in `the AWS docs <https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html>`_.

         .. tab-item:: Azure
            :sync: Azure

            Log in using ``az login``, then configure your credentials with ``az account set -s <subscription_id>``.

         .. tab-item:: GCP
            :sync: GCP

            Set the ``GOOGLE_APPLICATION_CREDENTIALS`` environment variable as described in `the GCP docs <https://cloud.google.com/docs/authentication/getting-started>`_.

   .. tab-item:: Community Supported
      :sync: Community Supported

      .. tab-set::

         .. tab-item:: Aliyun
            :sync: Aliyun

            Obtain and set the AccessKey pair of the Aliyun account as described in `the docs <https://www.alibabacloud.com/help/en/doc-detail/175967.htm>`__.

            Make sure to grant the necessary permissions to the RAM user and set the AccessKey pair in your cluster config file.
            Refer to the provided `aliyun/example-full.yaml </ray/python/ray/autoscaler/aliyun/example-full.yaml>`__ for a sample cluster config.

         .. tab-item:: vSphere
            :sync: vSphere

            Make sure Ray supervisor service is up and running as per `the Ray-on-VCF docs <https://github-vcf.devops.broadcom.net/vcf/vmray>`


Create a (basic) Python application
-----------------------------------

We will write a simple Python application that tracks the IP addresses of the machines that its tasks are executed on:

.. code-block:: python

    from collections import Counter
    import socket
    import time

    def f():
        time.sleep(0.001)
        # Return IP address.
        return socket.gethostbyname("localhost")

    ip_addresses = [f() for _ in range(10000)]
    print(Counter(ip_addresses))

Save this application as ``script.py`` and execute it by running the command ``python script.py``. The application should take 10 seconds to run and output something similar to ``Counter({'127.0.0.1': 10000})``.

With some small changes, we can make this application run on Ray (for more information on how to do this, refer to :ref:`the Ray Core Walkthrough <core-walkthrough>`):

.. code-block:: python

    from collections import Counter
    import socket
    import time

    import ray

    ray.init()

    @ray.remote
    def f():
        time.sleep(0.001)
        # Return IP address.
        return socket.gethostbyname("localhost")

    object_ids = [f.remote() for _ in range(10000)]
    ip_addresses = ray.get(object_ids)
    print(Counter(ip_addresses))

Finally, let's add some code to make the output more interesting:

.. code-block:: python

    from collections import Counter
    import socket
    import time

    import ray

    ray.init()

    print('''This cluster consists of
        {} nodes in total
        {} CPU resources in total
    '''.format(len(ray.nodes()), ray.cluster_resources()['CPU']))

    @ray.remote
    def f():
        time.sleep(0.001)
        # Return IP address.
        return socket.gethostbyname("localhost")

    object_ids = [f.remote() for _ in range(10000)]
    ip_addresses = ray.get(object_ids)

    print('Tasks executed')
    for ip_address, num_tasks in Counter(ip_addresses).items():
        print('    {} tasks on {}'.format(num_tasks, ip_address))

Running ``python script.py`` should now output something like:

.. parsed-literal::

    This cluster consists of
        1 nodes in total
        4.0 CPU resources in total

    Tasks executed
        10000 tasks on 127.0.0.1

Launch a cluster on a cloud provider
------------------------------------

To start a Ray Cluster, first we need to define the cluster configuration. The cluster configuration is defined within a YAML file that will be used by the Cluster Launcher to launch the head node, and by the Autoscaler to launch worker nodes.

A minimal sample cluster configuration file looks as follows:

.. tab-set::

   .. tab-item:: Ray Team Supported
      :sync: Ray Team Supported

      .. tab-set::

         .. tab-item:: AWS
            :sync: AWS

            .. literalinclude:: ../../../../python/ray/autoscaler/aws/example-minimal.yaml
               :language: yaml

         .. tab-item:: Azure
            :sync: Azure

            .. code-block:: yaml

                # An unique identifier for the head node and workers of this cluster.
                cluster_name: minimal

                # Cloud-provider specific configuration.
                provider:
                    type: azure
                    location: westus2
                    resource_group: ray-cluster

                # How Ray will authenticate with newly launched nodes.
                auth:
                    ssh_user: ubuntu
                    # you must specify paths to matching private and public key pair files
                    # use `ssh-keygen -t rsa -b 4096` to generate a new ssh key pair
                    ssh_private_key: ~/.ssh/id_rsa
                    # changes to this should match what is specified in file_mounts
                    ssh_public_key: ~/.ssh/id_rsa.pub

         .. tab-item:: GCP
            :sync: GCP

            .. code-block:: yaml

                # A unique identifier for the head node and workers of this cluster.
                cluster_name: minimal

                # Cloud-provider specific configuration.
                provider:
                    type: gcp
                    region: us-west1

   .. tab-item:: Community Supported
      :sync: Community Supported

      .. tab-set::

         .. tab-item:: Aliyun
            :sync: Aliyun

            Please refer to `example-full.yaml </ray/python/ray/autoscaler/aliyun/example-full.yaml>`__.

            Make sure your account balance is not less than 100 RMB, otherwise you will receive the error `InvalidAccountStatus.NotEnoughBalance`.

         .. tab-item:: vSphere
            :sync: vSphere

            .. literalinclude:: ../../../../python/ray/autoscaler/vsphere/example-minimal.yaml
               :language: yaml


Save this configuration file as ``config.yaml``. You can specify a lot more details in the configuration file: instance types to use, minimum and maximum number of workers to start, autoscaling strategy, files to sync, and more. For a full reference on the available configuration properties, please refer to the :ref:`cluster YAML configuration options reference <cluster-config>`.

After defining our configuration, we will use the Ray cluster launcher to start a cluster on the cloud, creating a designated "head node" and worker nodes. To start the Ray cluster, we will use the :ref:`Ray CLI <ray-cluster-cli>`. Run the following command:

.. code-block:: shell

    $ ray up -y config.yaml

Running applications on a Ray Cluster
-------------------------------------

We are now ready to execute an application on our Ray Cluster.
``ray.init()`` will now automatically connect to the newly created cluster.

As a quick example, we execute a Python command on the Ray Cluster that connects to Ray and exits:

.. code-block:: shell

    $ ray exec config.yaml 'python -c "import ray; ray.init()"'
    2022-08-10 11:23:17,093 INFO worker.py:1312 -- Connecting to existing Ray cluster at address: <remote IP address>:6379...
    2022-08-10 11:23:17,097 INFO worker.py:1490 -- Connected to Ray cluster.

You can also optionally get a remote shell using ``ray attach`` and run commands directly on the cluster. This command will create an SSH connection to the head node of the Ray Cluster.

.. code-block:: shell

    # From a remote client:
    $ ray attach config.yaml

    # Now on the head node...
    $ python -c "import ray; ray.init()"

For a full reference on the Ray Cluster CLI tools, please refer to :ref:`the cluster commands reference <cluster-commands>`.

While these tools are useful for ad-hoc execution on the Ray Cluster, the recommended way to execute an application on a Ray Cluster is to use :ref:`Ray Jobs <jobs-quickstart>`. Check out the :ref:`quickstart guide <jobs-quickstart>` to get started!

Deleting a Ray Cluster
----------------------

To shut down your cluster, run the following command:

.. code-block:: shell

    $ ray down -y config.yaml