1. AI Model Deployment with DigitalOcean Droplets


    Deploying an AI model on DigitalOcean Droplets involves several steps. You need to create a droplet which acts as a virtual private server, set up SSH keys to securely access the droplet, and configure your machine with the necessary environment to run the AI model. Here's a brief overview, followed by a Pulumi program in Python that demonstrates how to accomplish this deployment:

    1. Create an SSH Key: Secure access to your droplet is essential. You'll create an SSH key that will be used to authenticate securely without using passwords.

    2. Provision a DigitalOcean Droplet: You'll define a droplet which is a scalable virtual machine that runs on DigitalOcean infrastructure. You can define the size, image, region, and other configurations for your droplet.

    3. Access and Setup the Droplet: After the droplet is created, you'll be able to SSH into it and set up the necessary software environment to run your AI model. This could include installing libraries, downloading the model, and setting up a web server if you're serving the model as an API.

    4. Deploy the AI Model: Finally, you will deploy the AI model onto the droplet so that it can be used for inference.

    Below is a fully working Pulumi Python program that sets up a DigitalOcean droplet for AI model deployment.

    import pulumi import pulumi_digitalocean as digitalocean # Define configuration variables for the droplet droplet_name = "ai-model-deployment" droplet_size = "s-1vcpu-2gb" # This is the slug of the machine type droplet_image = "ubuntu-20-04-x64" # Using Ubuntu 20.04 image droplet_region = "nyc3" # New York 3 datacenter # Generate a new SSH key to use with your droplet ssh_key = digitalocean.SshKey("ai-model-key", public_key="ssh-rsa AAAAB3NzaC1yc2EAAAADAQABAAABAQC3... user@example.com") # Create a new DigitalOcean droplet for the AI model deployment droplet = digitalocean.Droplet("ai-model-deployment-droplet", image=droplet_image, name=droplet_name, region=droplet_region, size=droplet_size, ssh_keys=[ssh_key.id], tags=["ai-model"]) # Export the IP address of the Droplet pulumi.export("droplet_ip", droplet.ipv4_address) # For more information on DigitalOcean Droplet resources in Pulumi: # https://www.pulumi.com/registry/packages/digitalocean/api-docs/droplet/ # Information on the DigitalOcean SSH Keys: # https://www.pulumi.com/registry/packages/digitalocean/api-docs/sshkey/

    This program performs the following actions:

    • Imports the necessary Pulumi and DigitalOcean packages.
    • Defines configuration for the droplet, like the name, size, image, and region.
    • Creates an SshKey resource, which uploads a public SSH key to your DigitalOcean account. This step assumes you've already created an SSH key and have the public key available.
    • Defines a Droplet resource with the specified configuration and references the ssh_key by its ID.
    • Exports the IP address of the droplet, which you'll use to SSH into it.

    After the droplet is provisioned, you can SSH into it using the private key associated with the public SSH key you provided. Then, you can configure the software environment, download or copy your AI model, and run it as needed.

    Remember to replace the placeholder SSH public key in the ssh_key resource with your actual SSH public key.

    To run this program:

    1. Ensure Pulumi is installed and configured with DigitalOcean access tokens. If not, follow the Getting Started with Pulumi guide.
    2. Save this program in a file named __main__.py.
    3. Run pulumi up in the terminal within the directory that contains the program. This will create the resources on DigitalOcean as described in the code.

    When the command completes successfully, it will output the IP address of the droplet, which you can use to access your server.