GPU-Accelerated VMs for Large Language Model Inference

Introduction

In this guide, we will set up GPU-accelerated virtual machines (VMs) for large language model inference using Pulumi and AWS. This involves creating EC2 instances with GPU capabilities, configuring the necessary security groups, and setting up the required IAM roles.

Step-by-Step Explanation

Step 1: Create an EC2 Instance with GPU

We will create an EC2 instance with GPU capabilities. AWS offers various instance types with GPU support, such as p3 and g4 instances. We will use the p3.2xlarge instance type for this example.

Step 2: Configure Security Groups

We need to configure security groups to allow access to the EC2 instance. This includes setting up rules for SSH access and any other necessary ports for our application.

Step 3: Set Up IAM Roles

IAM roles are required to grant the necessary permissions to our EC2 instance. We will create an IAM role with the appropriate policies attached.

Step 4: Deploy the Infrastructure

Using Pulumi, we will deploy the infrastructure defined in the previous steps.

Summary

In this guide, we set up GPU-accelerated VMs for large language model inference using Pulumi and AWS. We created an EC2 instance with GPU capabilities, configured security groups, set up IAM roles, and deployed the infrastructure using Pulumi.

By following these steps, you can leverage GPU-accelerated VMs for efficient large language model inference on AWS.

Full Code Example

import * as pulumi from "@pulumi/pulumi";
import * as aws from "@pulumi/aws";

// Create a Security Group
const securityGroup = new aws.ec2.SecurityGroup("gpu-sg", {
    description: "Allow SSH and application access",
    ingress: [
        { protocol: "tcp", fromPort: 22, toPort: 22, cidrBlocks: ["0.0.0.0/0"] }, // SSH access
        { protocol: "tcp", fromPort: 80, toPort: 80, cidrBlocks: ["0.0.0.0/0"] }, // HTTP access
        { protocol: "tcp", fromPort: 443, toPort: 443, cidrBlocks: ["0.0.0.0/0"] }, // HTTPS access
    ],
    egress: [
        { protocol: "-1", fromPort: 0, toPort: 0, cidrBlocks: ["0.0.0.0/0"] }, // Allow all outbound traffic
    ],
});

// Create an IAM Role
const role = new aws.iam.Role("gpu-role", {
    assumeRolePolicy: JSON.stringify({
        Version: "2012-10-17",
        Statement: [
            {
                Action: "sts:AssumeRole",
                Principal: {
                    Service: "ec2.amazonaws.com",
                },
                Effect: "Allow",
                Sid: "",
            },
        ],
    }),
});

// Attach a Policy to the Role
const rolePolicy = new aws.iam.RolePolicy("gpu-role-policy", {
    role: role.id,
    policy: JSON.stringify({
        Version: "2012-10-17",
        Statement: [
            {
                Action: [
                    "ec2:Describe*",
                    "s3:ListBucket",
                    "s3:GetObject",
                ],
                Effect: "Allow",
                Resource: "*",
            },
        ],
    }),
});

// Create an EC2 Instance with GPU
const instance = new aws.ec2.Instance("gpu-instance", {
    instanceType: "p3.2xlarge",
    ami: "ami-0c55b159cbfafe1f0", // Amazon Linux 2 AMI (HVM), SSD Volume Type
    securityGroups: [securityGroup.name],
    iamInstanceProfile: role.name,
    tags: {
        Name: "gpu-instance",
    },
});

export const instanceId = instance.id;
export const publicIp = instance.publicIp;