1. Persistent Data Storage for AI with DigitalOcean Volumes


    To create persistent data storage for AI applications using DigitalOcean, we can make use of DigitalOcean Volumes. Persistent Volumes (PVs) are block storage devices that can be attached to droplets, which are essentially VM instances in DigitalOcean. These volumes provide extra disk space for your droplets and can be used independently of the lifecycle of a droplet.

    When working with persistent data storage, you want to ensure that your stored data:

    1. Outlives the lifecycle of individual droplets (VM instances).
    2. Is performant enough to serve data-intensive operations, which is typical in AI workloads.

    Pulumi's infrastructure as code model allows us to define, deploy, and manage this kind of storage easily. Below, I will demonstrate a Pulumi program that uses DigitalOcean's Volume resource to create a persistent volume, and then attaches it to a droplet.

    Step by Step Explanation:

    1. DigitalOcean Volume: We define a volume that specifies the size and other parameters, such as the region where the volume resides. This volume will serve as our persistent storage.

    2. DigitalOcean Droplet: We create a droplet which is a Linux-based virtual machine to which we will eventually attach our volume. Droplets are powerful and scalable virtual machines that can be used to run applications.

    3. Volume Attachment: Once the volume and the droplet are provisioned, we will attach the volume to the droplet. This means that the volume will be accessible by the droplet and can be used for persistent storage.

    Here is how this can be done using Pulumi with Python:

    import pulumi import pulumi_digitalocean as digitalocean # Step 1: Create a persistent volume # The `Volume` class represents a block storage volume for use with DigitalOcean Droplets. # Here, we create a 10 GB volume that will store data in the NYC3 region. persistent_volume = digitalocean.Volume("ai-persistent-volume", region="nyc3", size=10, # Size of the volume in GiB ) # Step 2: Create a droplet # The `Droplet` class represents a Droplet in DigitalOcean. # This is basically a virtual machine to which we will attach our volume later. my_droplet = digitalocean.Droplet("ai-droplet", image="ubuntu-20-04-x64", # You can choose any available image region="nyc3", size="s-1vcpu-1gb", # The type of the Droplet which defines it's resources like CPU, RAM, etc. ) # Step 3: Attach the volume to the droplet # The `VolumeAttachment` class represents a block storage volume attached to a droplet. volume_attachment = digitalocean.VolumeAttachment("ai-volume-attachment", droplet_id=my_droplet.id, # Attach to our droplet volume_id=persistent_volume.id, # The ID of the volume to be attached ) # Output the volume's and droplet's details pulumi.export("droplet_name", my_droplet.name) pulumi.export("volume_name", persistent_volume.name)

    In the above program:

    • We create a Volume and specify that we want it to be 10GB and in the NYC3 region.
    • We create a Droplet which will be our VM instance, specifying its image and size.
    • We attach our volume to our droplet using VolumeAttachment.

    With these resources in place, you'll have a persistent volume that can be used to store AI models or data sets, and this storage will remain even if you destroy or recreate your droplet.

    To go further, you can also add additional logic to format the drive, mount it to a specific directory, or even automate data backups. However, those tasks would generally be handled by an initialization script or configuration management tool, rather than directly from within the Pulumi program.