How to launch Ray Clusters on AWS

1. Introduction to Ray
2. Ray and AWS a powerful combination
3. AWS Environment SetUp to Launch Ray Cluster
4. Scaling and Distributing Ray workloads on AWS

Introduction to Ray

Ray and AWS a powerful combination

AWS Environment SetUp to Launch Ray Cluster

1. AWS Account: You will need an AWS account to create and manage cloud resources.
2. Python 3.x is installed.
3. AWS user with IAM permission to create EC2 instances
4. The AWS CLI is installed and credentials are configured to authenticate the IAM user. Check out this how to guide :
   (https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html)

Step 1: Launch an EC2 instance

1. Log in to your AWS Management Console.
2. Navigate to the EC2 dashboard.
3. Click on "Launch Instance" to create a new virtual machine.
4. Choose an Amazon Machine Image (AMI) that suits your needs. A standard Linux distribution like Amazon Linux or Ubuntu Server is a good starting point.
5. Select an instance type based on your workload requirements. Ray can be run on various instance types, but for experimentation, a t2.micro (free-tier eligible) instance is sufficient.
6. Configure instance details, storage, security groups, and add tags if needed.
7. Review your settings and launch the instance. You'll need an SSH key pair to access the instance securely.

Step 2: Connect to Your EC2 Instance

Step 3: Install Ray

Step 4: Write your first Ray program on EC2

1. Make a new directory and create python file raytest
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   mkdir RayTest
   cd RayTest
   nano raytest.py
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   import ray
   
   ray.init()
   
   @ray.remote
   def say_hello():
   return "Hello, Let’s get started with Ray on AWS!"
   
   futures = [say_hello.remote() for _ in range(5)]
   
   results = ray.get(futures)
   print(results)
   
   ray.shutdown()
2. Run your program using the below command
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   python rayTest.py
   You'll see the distributed power of Ray in action as it prints out ' Hello, Let’s get started with Ray on AWS!'

Scaling and Distributing Ray workloads on AWS

1. Launch additional EC2 instances following Step 1.
2. Install Ray on each instance as in Step3.
3. On the second instance modify the init block to use the head node i.e. public ip of the first instance.
   This allows your program to use multiple nodes for distributed computing.
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   ray.init(address="ray://<ip address of the first instance>:6379")
4. Execute your program, and Ray will distribute tasks across all connected instances.

• ruchi-mishra
• naga-gaddamu