Versioning AI Dataset Samples in GCP Storage Buckets.

Question

Pulumi · Accepted Answer

In order to version AI dataset samples in Google Cloud Storage (GCP Storage), you'll need to do a few key things:

1. Create a GCP Storage Bucket with versioning enabled. This means that each time an object is overwritten or deleted, a historical copy of the object will be kept. This feature is crucial for maintaining versions of your AI datasets.

2. Upload objects to the GCP Storage Bucket. These objects can be your AI dataset files that you want to version.

Below is a Pulumi program in Python that demonstrates how to create a GCP Storage Bucket with versioning enabled and upload a sample dataset to it. This sample assumes you have credentials to authenticate with GCP, and you've already installed the `pulumi-gcp` package.

```python
import pulumi
import pulumi_gcp as gcp

# Create a GCP storage bucket with versioning enabled
bucket = gcp.storage.Bucket("ai-dataset-bucket",
    location="US",
    versioning={
        "enabled": True
    }
)

# Upload a local file to the bucket as an object.  In practice, this
# would be your AI dataset file.
bucket_object = gcp.storage.BucketObject("ai-dataset-sample",
    bucket=bucket.name,
    source=pulumi.FileAsset("path_to_your_dataset_file_here") # replace with your dataset file path
)

# Export the URLs of the bucket and object to access them later
pulumi.export('bucket_name', bucket.url)
pulumi.export('dataset_object_name', bucket_object.name)
```

Here's what each part of the program does:

- `gcp.storage.Bucket` creates a new storage bucket. The `versioning` parameter is set to a dictionary with `"enabled": True` to turn on versioning for this bucket. Replace `"US"` with the region that best suits you.

- `gcp.storage.BucketObject` represents an object within the bucket. We use `pulumi.FileAsset("path_to_your_dataset_file_here")` to specify the file you'd like to upload into the bucket. Replace `"path_to_your_dataset_file_here"` with the actual file path to your dataset.

- `pulumi.export` statements make the URL of the bucket and the name of the object available as outputs when the Pulumi program is run, so you can access them easily.

When running this program with Pulumi, it will provision a new GCP Storage Bucket with versioning enabled and upload your AI dataset file to it. If you change the object later (by running the Pulumi program with a different file path, for example), GCP will keep the previous versions of the file accessible, enabling you to go back to earlier data if required.

Before running this Pulumi program, ensure that the Pulumi CLI is installed, the GCP plugin is installed, and your GCP credentials are set up. Then, simply run `pulumi up` to create the resources. If you have multiple versions of a dataset that you would like to manage, just modify the `source` attribute of `BucketObject` with the new file path and re-run `pulumi up`. GCP will keep the previous versions of the object stored due to the enabled versioning feature.