Staging AI Model Rollouts with DigitalOcean Snapshots

Question

Pulumi · Accepted Answer

When managing AI model deployments, especially in a staging environment, it's crucial to be able to quickly roll back to a previous state if something goes wrong with the latest iteration of your AI model. Using DigitalOcean, snapshot features can serve as a point-in-time backup and facilitate easy rollbacks.

DigitalOcean Snapshots are essentially read-only images of your Droplet or Volumes that can be used to create new Droplets or Volumes with the same state. This functionality is valuable when you want to freeze the state of your infrastructure before making any changes, such as deploying a new AI model, so you can revert back if necessary.

Below is a Pulumi program written in Python that demonstrates how to create DigitalOcean Droplet and Volume snapshots. It uses Pulumi's DigitalOcean provider to create a new Droplet and Volume and subsequently takes snapshots of each. The resulting snapshots can be used to create new Droplet and Volume instances that preserve the staged AI model's setup.

```python
import pulumi
import pulumi_digitalocean as digitalocean

# Create a new Droplet to host your AI model.
droplet = digitalocean.Droplet("aiModelDroplet",
    image="ubuntu-20-04-x64",  # Your preferred Droplet image.
    region="nyc3",  # Select your preferred region.
    size="s-1vcpu-1gb",  # Select the appropriate droplet size.
    ssh_keys=[YOUR_SSH_KEY],  # Replace with your SSH key for secure access.
    backups=True,  # Enable backups for the Droplet.
    ipv6=True,  # Enable IPv6 for the Droplet (optional).
)

# Create a new Volume to store persistent data.
volume = digitalocean.Volume("aiModelVolume",
    region=droplet.region,  # Ensure that the Volume is in the same region as the Droplet.
    size=10,  # Size of the Volume in GB.
    description="Persistent storage for AI model data",
)

# Take a snapshot of the Droplet.
droplet_snapshot = digitalocean.DropletSnapshot("aiModelDropletSnapshot",
    droplet_id=droplet.id,  # ID of the Droplet to create a snapshot for.
)

# Take a snapshot of the Volume.
volume_snapshot = digitalocean.VolumeSnapshot("aiModelVolumeSnapshot",
    volume_id=volume.id,  # ID of the Volume to create a snapshot for.
)

# Export the IDs of the snapshots to be accessed outside of Pulumi if needed.
pulumi.export('droplet_snapshot_id', droplet_snapshot.id)
pulumi.export('volume_snapshot_id', volume_snapshot.id)
```

Replace `[YOUR_SSH_KEY]` with your actual DigitalOcean SSH key information to enable secure access to the Droplet.

The program begins by defining a Droplet and Volume suitable for hosting and storing your AI model's data. It then creates snapshots of both the Droplet and the Volume. These snapshots are the key to facilitating the rollback. If a deployment to the Droplet goes awry, you can create a new Droplet from the snapshot, which will have the state of the existing Droplet at the time the snapshot was taken.

In the end, the program exports the IDs of both the Droplet and Volume snapshots. These IDs can be used to manage the snapshots outside the Pulumi environment, such as in the DigitalOcean dashboard or API.

Make sure you have the DigitalOcean Pulumi provider set up and configurations in place for your Pulumi environment to execute this code successfully.