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Ray Cluster Quick Start

This quick start demonstrates the capabilities of the Ray cluster. Using the Ray cluster, we'll take a sample application designed to run on a laptop and scale it up in the cloud. Ray will launch clusters and scale Python with just a few commands.

About the demo

This demo will walk through an end-to-end flow:

  1. Create a (basic) Python application.
  2. Launch a cluster on a cloud provider.
  3. Run the application in the cloud.

Requirements

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, Azure or GCP).

Setup

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

.. tabs::
    .. group-tab:: AWS

        .. code-block:: shell

            $ pip install -U ray boto3

    .. group-tab:: Azure

        .. code-block:: shell

            $ pip install -U ray azure-cli azure-core

    .. group-tab:: GCP

        .. code-block:: shell

            $ pip install -U ray google-api-python-client

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

.. tabs::
    .. group-tab:: AWS

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

    .. group-tab:: Azure

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

    .. group-tab:: GCP

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

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:

from collections import Counter
import socket
import time

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

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>`):

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(socket.gethostname())

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:

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(socket.gethostname())

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:

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:

.. tabs::
    .. group-tab:: AWS

        .. code-block:: yaml

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

            # Cloud-provider specific configuration.
            provider:
                type: aws
                region: us-west-2

    .. group-tab:: 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

    .. group-tab:: 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

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-cli>`. Run the following command:

$ ray up -y config.yaml

Run the application in the cloud

We are now ready to execute the application in across multiple machines on our Ray cloud cluster. First, we need to edit the initialization command ray.init() in script.py. Change it to

ray.init(address='auto')

This will allow Ray to connect to the remote cluster.

Next, run the following command:

$ ray submit config.yaml script.py

The output should now look similar to the following:

This cluster consists of
    3 nodes in total
    6.0 CPU resources in total

Tasks executed
    3425 tasks on xxx.xxx.xxx.xxx
    3834 tasks on xxx.xxx.xxx.xxx
    2741 tasks on xxx.xxx.xxx.xxx

In this sample output, 3 nodes were started. If the output only shows 1 node, you may want to increase the secs in time.sleep(secs) to give Ray more time to start additional nodes.

The Ray CLI offers additional functionality. For example, you can monitor the Ray cluster status with ray monitor config.yaml, and you can connect to the cluster (ssh into the head node) with ray attach config.yaml. For a full reference on the Ray CLI, please refer to :ref:`the cluster commands reference <cluster-commands>`.

To finish, don't forget to shut down the cluster. Run the following command:

$ ray down -y config.yaml