Deploying Dedicated AI Model Training Clusters on Vultr Bare Metal

Question

Pulumi · Accepted Answer

To deploy dedicated AI model training clusters on Vultr using bare metal servers, you'll be creating infrastructure that likely consists of the following components:

Bare Metal Servers: These serve as the backbone of the cluster, where the actual model training will be carried out. Pulumi's vultr.BareMetalServer resource will be used to create and manage these servers.
Startup Script: To configure the bare metal servers with necessary dependencies and training code, you may want to use a startup script. The vultr.StartupScript resource allows you to automate this process.
Virtual Private Cloud (VPC): To ensure that your servers are on an isolated network, vultr.Vpc can be used to create and manage a VPC where your bare metal servers will reside.
SSH Keys: To securely access your servers, vultr.SshKey resource is used to manage SSH keys which can be used to authenticate.

In the following Pulumi program, we will set up the bare metal servers with a startup script that prepares them for AI model training. We also create a VPC to ensure network isolation.

Before running this Pulumi program, make sure you have:

Installed Pulumi CLI and set it up (https://www.pulumi.com/docs/get-started/).
Configured Pulumi to use the Vultr provider (https://www.pulumi.com/registry/packages/vultr/installation-configuration/).
Have the necessary Vultr API token set up for authentication.

Here's what the Pulumi program will look like:

import pulumi
import pulumi_vultr as vultr

# Create a Virtual Private Cloud to ensure the bare metal servers are on a secure, isolated network.
vpc = vultr.Vpc("training-vpc",
    region="ewr", # Replace with the desired region
    v4_subnet="10.10.0.0", # Your VPC subnet. Ensure it's a private range and large enough for your needs.
    v4_subnet_mask=24,
)

# Add a startup script to your bare metal server for installing dependencies and setting up the environment.
startup_script = vultr.StartupScript("training-startup-script",
    script="""
        #!/bin/bash
        # Install Docker, NVIDIA drivers, and other dependencies.
        apt-get update && apt-get install -y docker.io nvidia-docker2
        # Start Docker and any other necessary services.
        systemctl start docker
        # Your additional setup commands go here.
    """,
    type="boot", # This ensures your script runs at boot.
)

# Create a new SSH key resource for authentication with the bare metal servers.
ssh_key = vultr.SshKey("training-ssh-key",
    ssh_key="<your_public_ssh_key_here>", # Replace with your actual SSH public key.
)

# Deploying the bare metal servers for the AI model training cluster.
bare_metal_server = vultr.BareMetalServer("ai-training-cluster",
    region="ewr", # Replace with the region you wish to deploy in.
    plan="vc2-4c-8gb", # Choose a plan that meets your AI workload's requirements.
    os_id=270, # The OS ID. Here we are using the ID for Ubuntu 20.04 x64.
    script_id=startup_script.id, # The ID of the created startup script resource.
    ssh_keys=[ssh_key.id], # The ID of the created SSH key resource.
    vpc_id=vpc.id, # The ID of the created VPC resource.
    hostname="ai-cluster-node", # Set the hostname for your server.
    tag=["ai-training"], # Tags for easier management and categorization of your resources.
)

pulumi.export("vpc_id", vpc.id)
pulumi.export("server_id", bare_metal_server.id)
pulumi.export("server_ip", bare_metal_server.ip)

Make sure to replace placeholder strings such as <your_public_ssh_key_here> with actual values that apply to your situation. Also, adjust the regions, subnet configuration, plan, and operating system as necessary for your specific needs. The start-up script should also be customized to install and configure the software that your AI training workloads require.

After running this program with pulumi up, it will provision the resources as defined and output the identifiers and IP address of the server which you can use to access your AI training cluster.