Getting Started#

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.

For launching a Ray cluster manually, you can refer to the on-premise cluster setup guide.

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, GCP, Azure, Aliyun, or vSphere).

Setup#

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

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

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

Configure your credentials in ~/.aws/credentials as described in the AWS docs.

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 the Ray 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:

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

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

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 cluster YAML configuration options reference.

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 Ray CLI. Run the following command:

$ 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:

$ 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.

# 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 the cluster commands reference.

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 Ray Jobs. Check out the quickstart guide to get started!

Deleting a Ray Cluster#

To shut down your cluster, run the following command:

$ ray down -y config.yaml