Versioned Machine Learning Datasets in DigitalOcean Spaces

Question

Pulumi · Accepted Answer

To accomplish your goal of handling versioned machine learning datasets using DigitalOcean, we'll be creating and configuring a DigitalOcean Spaces bucket. Spaces is an object storage service that makes it easy and cost-effective to store and serve large amounts of data. Versioning is a useful feature that keeps a history of each file in the bucket, and is beneficial for datasets being used in machine learning, where we need to track and revert to previous data versions.

Here's what we will do:
1. **Create a Spaces Bucket**: This is where your datasets will be stored.
2. **Enable Versioning**: To keep multiple versions of each dataset object in your bucket, we enable bucket versioning.
3. **Upload a Dataset Object**: As an example, we'll upload a file that represents a dataset. In a real-world scenario, you'd upload your actual datasets.
4. **Set a Lifecycle Rule (Optional)**: It's sometimes useful to automatically manage the lifecycle of objects within the bucket, like purging older versions after some time.

Below is a Pulumi program in Python that performs these steps. I'll explain each part of the program as comments within the code.

```python
import pulumi
import pulumi_digitalocean as digitalocean

# Create a new DigitalOcean Spaces Bucket to store the datasets.
dataset_bucket = digitalocean.SpacesBucket("ml-dataset-bucket",
    # Choose a name specific to your project or company name.
    name="mycompany-datasets",
    # The region where you want to create your Spaces bucket.
    region="nyc3",
    # Enabling versioning on the bucket.
    versioning={
        "enabled": True
    }
)

# For demonstration, we'll upload a 'dummy' dataset file to the bucket.
# In an actual scenario, this would be your machine learning dataset.
dataset_object = digitalocean.SpacesBucketObject("initial-dataset-version",
    bucket=dataset_bucket.name,
    key="datasets/iris.csv",  # Assuming 'iris.csv' is a dataset file you want to upload.
    content_type="text/csv",
    # Here you specify the path or the data itself.
    # Source could be a file path, or you can use `content` to specify raw data.
    source=pulumi.FileAsset("path/to/your/dataset/iris.csv"),
    # Specifying the region is mandatory for Space Bucket Objects.
    region=dataset_bucket.region
)

# Optionally, if you want to manage the lifecycle of the objects in the bucket,
# for instance, to automatically delete noncurrent versions of objects after a certain period.
lifecycle_rule = digitalocean.SpacesBucketLifecycleRuleArgs(
    id="lifecycle-rule",
    prefix="datasets/", # Apply the rule to files in the 'datasets' folder.
    enabled=True,
    noncurrentVersionExpiration={
        "days": 30 # Number of days until the noncurrent object versions expire.
    }
)

# Applying the lifecycle rule to our bucket.
dataset_bucket.lifecycle_rules = [lifecycle_rule]

# Exporting the bucket name and the dataset object URL.
pulumi.export("bucket_name", dataset_bucket.name)
pulumi.export("dataset_object_url", dataset_object.url)
```

This program defines a project in which a DigitalOcean Spaces bucket with versioning enabled is created to store ML datasets. It also shows how to upload an object, in this example, a CSV file, to the bucket. Remember to replace `"path/to/your/dataset/iris.csv"` with the actual file path of your dataset.

Please ensure that you have the `pulumi_digitalocean` plugin installed and configured with the appropriate access tokens for DigitalOcean. You can install the plugin using `pip install pulumi_digitalocean`.

To run the program:
- Save the code in a file (e.g., `main.py`).
- Run `pulumi up` in the same directory as your file to launch the Pulumi program which provisions the resources.

After running the program, the bucket name and object URL will be displayed as an output, which you can then use to access your datasets. The object URL points to the 'iris.csv' file in this example.

If you want to manage your dataset versions manually or programmatically, you'll use the functions provided by the DigitalOcean API or SDK to list, retrieve, and delete the various versions of your objects as needed.