High Performance Compute Clusters on EC2 for AI

Question

Pulumi · Accepted Answer

High-performance computing (HPC) clusters on Amazon EC2 are designed to run advanced compute-intensive applications, including artificial intelligence (AI) and machine learning workloads. These clusters utilize EC2 instances that are optimized for compute capacity, memory, and networking.

To set up an HPC cluster on EC2 for AI, you'd typically need the following:

EC2 instances configured as compute nodes. These could be instances from the C5, P3, or G4 families, which are optimized for compute-intensive tasks, machine learning, and GPU-based workloads respectively.
An EC2 placement group to ensure that the instances are physically located close to each other, reducing network latency and increasing communication speed between nodes.
Elastic Block Store (EBS) volumes for persistent and fast storage, attached to the EC2 instances.
A virtual private cloud (VPC) for network isolation and security.
Subnets, security groups, and an internet gateway (if necessary) to control network access to the instances.

I'll provide you with a Pulumi program that creates a simple EC2-based HPC cluster setup suitable for running AI workloads. Please note that this is a basic template and that real-world applications might require additional configurations for security, monitoring, and management.

Below is a Pulumi Python program that accomplishes the setup:

import pulumi
import pulumi_aws as aws

# Create a new VPC for our HPC cluster to ensure network isolation
vpc = aws.ec2.Vpc("hpc_vpc",
    cidr_block="10.0.0.0/16",
    enable_dns_hostnames=True,
    enable_dns_support=True,
    tags={"Name": "hpc_vpc"}
)

# Create an internet gateway for the VPC for instances that might need public internet access
igw = aws.ec2.InternetGateway("hpc_igw",
    vpc_id=vpc.id,
    tags={"Name": "hpc_igw"}
)

# Create a subnet within our VPC
subnet = aws.ec2.Subnet("hpc_subnet",
    vpc_id=vpc.id,
    cidr_block="10.0.1.0/24",
    map_public_ip_on_launch=True,
    availability_zone="us-west-2a",
    tags={"Name": "hpc_subnet"}
)

# Create a security group for our HPC cluster
security_group = aws.ec2.SecurityGroup("hpc_security_group",
    vpc_id=vpc.id,
    description="Security group for HPC cluster",
    tags={"Name": "hpc_security_group"}
)

# Create a placement group for EC2 instances. The "compute" strategy is ideal for HPC.
placement_group = aws.ec2.PlacementGroup("hpc_placement_group",
    strategy="cluster",
    tags={"Name": "hpc_placement_group"}
)

# Choose an appropriate instance type for AI workloads (e.g. C5 for compute or P3 for GPUs)
instance_type = "c5.large"

# Create EC2 instances as compute nodes for the HPC cluster
# Replace <ami_id> with an appropriate AMI ID from your region
# Typically an AI-optimized AMI with necessary software and drivers is used
instance = aws.ec2.Instance("hpc_instance",
    instance_type=instance_type,
    vpc_security_group_ids=[security_group.id],
    subnet_id=subnet.id,
    ami="<ami_id>", # Replace with a valid AI-optimized AMI ID for your region
    placement_group=placement_group.name,
    key_name="<your_keypair_name>", # Replace with your key pair name
    root_block_device=aws.ec2.InstanceRootBlockDeviceArgs(
        volume_type="gp2",
        volume_size=50, # In GB, adjust as necessary
    ),
    tags={"Name": "hpc_instance"}
)

# Export the IP address of the instance
pulumi.export("instance_public_ip", instance.public_ip)

Here's what the code does:

It begins by creating a new Virtual Private Cloud (VPC) to provide a secure, isolated network environment for your cluster.
It sets up an internet gateway for the VPC. This is useful if the EC2 instances need to access the internet, for example, to download updates or AI models.
It defines a subnet within the VPC to launch our instances into.
It creates a security group with the default settings. In a production environment, you should specify inbound and outbound rules according to your security requirements.
It creates an EC2 Placement Group with the 'cluster' strategy, which is a good fit for high-performance computing since it ensures that instances are located close enough for low-latency communication.
It then launches an EC2 instance within the subnet, security group, and placement group we created. The instance type is 'c5.large', but for GPU-based machine learning tasks, consider using 'p3.2xlarge' or another GPU-optimized instance type.

Make sure to replace <ami_id> with an actual Amazon Machine Image (AMI) ID that is suitable for your AI workload and <your_keypair_name> with your own key pair name for SSH access.

When you run your Pulumi program, use the pulumi up command to create the resources. Make sure you have the Pulumi CLI installed and configured with the necessary AWS credentials. This program will create the prescribed setup on AWS and provide you with the public IP address of the EC2 instance, which you can use to connect and manage your AI workloads.

Remember, to scale this into a full HPC cluster, you'd typically launch multiple EC2 instances and often manage them with a scheduler like AWS Batch or an open-source alternative like Slurm.