Versioning Model Checkpoints with DigitalOcean Spaces

Question

Pulumi · Accepted Answer

To accomplish the goal of versioning model checkpoints with DigitalOcean Spaces, we will be defining a DigitalOcean Spaces Bucket with versioning enabled. DigitalOcean Spaces is an object storage service that is compatible with the S3 API, which makes it useful for storing and versioning large amounts of unstructured data like model checkpoints.

Here's how it works:

- A **Spaces Bucket** is similar to an AWS S3 bucket; it's where your data will be stored. Each file (or object) saved in your bucket can be accessed through a unique endpoint.
- **Versioning** is a means to keep multiple variants of an object in the same bucket. When you enable versioning, you can retrieve, restore, and manage versions of the objects stored within your bucket. This is perfect for maintaining different iterations of model checkpoints.

Below is a Pulumi Python program that sets up a DigitalOcean Spaces bucket with versioning enabled, which you can use to store and manage your model checkpoints:

```python
import pulumi
import pulumi_digitalocean as digitalocean

# Create a new DigitalOcean Spaces bucket to store model checkpoints.
# We enable versioning so that each checkpoint iteration is preserved.
model_checkpoints_bucket = digitalocean.SpacesBucket('model-checkpoints-bucket',
    region='nyc3',  # Examples of region include 'nyc3', 'sfo2', 'ams3', etc.
    acl='private',  # Make the bucket private so that only authorized users can access its contents.
    versioning={
        'enabled': True  # Enable versioning to keep a history of checkpoint iterations.
    }
)

# Output the bucket endpoint for easy access.
bucket_endpoint = pulumi.Output.concat("https://", model_checkpoints_bucket.name, ".nyc3.digitaloceanspaces.com")
pulumi.export('bucket_endpoint', bucket_endpoint)
```

In this program, we first import the necessary Pulumi and DigitalOcean modules. We define a `SpacesBucket` instance named `model-checkpoints-bucket`, specifying the region where you would like to create your Space – for example, `'nyc3'`. Versioning is turned on by setting `'enabled': True` under the `versioning` argument of `SpacesBucket`.

Remember to replace `'nyc3'` with the region that you prefer to use. The `acl` attribute is set to `'private'`, which means that the objects in the bucket are not publicly accessible and require authorization for access.

We also provide an output, `bucket_endpoint`, which concatenates the bucket name with the DigitalOcean Spaces endpoint for the specified region, giving you a URL you can use to access the Space.

By enabling versioning, any file uploaded to this bucket will maintain a history of its versions, which is useful for tasks such as rolling back to a previous model if a new one isn't satisfactory.

Before running this program, ensure that you have the Pulumi CLI installed and configured with DigitalOcean access tokens. You can create a new Pulumi project and use this code to set up your infrastructure. After you run `pulumi up`, Pulumi will perform the necessary actions to create the resources defined in the program.