Snapshot Versioning for DigitalOcean-based ML Models

Question

Pulumi · Accepted Answer

As you're looking to implement snapshot versioning for machine learning (ML) models hosted on DigitalOcean, let me explain how you could use Pulumi to achieve this. DigitalOcean doesn't have a native machine learning service similar to AWS SageMaker or Azure Machine Learning. Instead, models hosted on DigitalOcean would likely be deployed on virtual machines (Droplets) or Kubernetes clusters (DOKS). The way to implement versioning in this case would be to handle the models as part of your application code or data, using DigitalOcean's snapshots for Droplets or volumes for backup and versioning.

To create a snapshot versioning system, you'll capture snapshots of either the entire virtual machine (if using Droplets) or specific volumes where your models are stored. Snapshotting an entire virtual machine or volume would create a point-in-time backup that you could revert back to or spin up new instances, effectively handling model versioning outside of the typical MLops tools.

Here's a Python program using Pulumi to automatically create snapshots of DigitalOcean Droplets:

```python
import pulumi
import pulumi_digitalocean as digitalocean

# Assume model_version and droplet_id would come from your application or state tracking system
model_version = "v1.2.3"  # An identifier for the model version
droplet_id = "your-droplet-id"  # The ID of the Droplet running the ML model

# Creating a snapshot of the Droplet with the current model version
droplet_snapshot = digitalocean.DropletSnapshot(f"ml-model-snapshot-{model_version}",
    droplet_id=droplet_id,
    name=f"ml-model-{model_version}-{droplet_id}")

# Export the snapshot URL so you can access it later
pulumi.export("snapshot_url", droplet_snapshot.url)
```

In this program, we import `pulumi`, which is the core Pulumi package. Additionally, we import `pulumi_digitalocean` which is the Pulumi provider for managing DigitalOcean resources.

We define `model_version` as a string that represents your machine learning model's version. In actual practice, this could be dynamically set based on your application versioning logic. The `droplet_id` should be the ID of the Droplet you wish to snapshot.

Next, we create a `DropletSnapshot` resource which represents a snapshot of the specified Droplet. We use the version and Droplet ID to name the snapshot uniquely.

Finally, we export the URL of the snapshot. This URL can be used to access the snapshot outside of Pulumi, such as in your continuous deployment pipeline, and is also printed out as part of the Pulumi stack's outputs after an update.

Please make sure to replace `"your-droplet-id"` with the actual ID of the Droplet running your ML model. Keep in mind snapshot operations can take several minutes, depending on the size of the Droplet or volume, thus they may impact the availability of the services running on the Droplet.

Note that for data stored on volumes, the approach would be similar. You would create `VolumeSnapshot` resources, providing the volume ID instead of the Droplet ID.

This simple Pulumi program provides the basis for a robust versioning system for your ML models on DigitalOcean. Depending on the complexity and needs of your ML infrastructure, you may want to consider additional tools for model versioning, such as DVC (Data Version Control) or tracking version metadata in a database.