Versioned Data Storage for Machine Learning with GCP

Question

Pulumi · Accepted Answer

To create a versioned data storage for Machine Learning (ML) with Google Cloud Platform (GCP), we will utilize several cloud services and Pulumi resources dedicated to data storage and management. Here's a brief overview of the GCP services we'll be using to accomplish this:

1. **Google Cloud Storage (GCS)**: This is a highly durable and scalable object storage service, which is ideal for storing large datasets for ML. It also supports object versioning, which we'll enable to keep track of various versions of each stored object.

2. **Google Cloud Bigtable (optional)**: This is a high-performance NoSQL database service suitable for analytical and operational workloads in ML. If our ML dataset requires fast read and write access with high throughput, we might consider using Bigtable instead of, or in addition to, GCS.

3. **Google Cloud Source Repositories (optional)**: If our ML project would benefit from versioned code or configuration files, we can use this service as a fully-featured, scalable, private Git repository.

In Pulumi, we will define the following resources to set up versioned data storage:

- `Bucket`: Represents a GCS bucket where ML data will be stored. We'll configure it to enable versioning.
- `BucketObject`: Represents objects within the GCS bucket. Their versioning will be automatically managed by GCS once versioning is enabled on the `Bucket`.
- (Optional) `Instance`: If needed, represents a Bigtable instance for fast access to structured data.
- (Optional) `Table`: A table within Bigtable which holds actual data.

Here's a Python program using Pulumi's GCP package (`pulumi_gcp`). Note that before running this program, you should have Pulumi CLI installed along with GCP SDK and have your GCP credentials configured for Pulumi:

```python
import pulumi
import pulumi_gcp as gcp

# Create a Google Cloud Storage bucket
ml_data_bucket = gcp.storage.Bucket("mlDataBucket",
    location="US",
    versioning={
        "enabled": True,
    }
)

# Example of uploading an object to the GCS bucket
# Here, "my_ml_dataset.csv" represents a dataset file that you want to upload.
ml_dataset_object = gcp.storage.BucketObject("mlDataset",
    bucket=ml_data_bucket.name,
    source=pulumi.FileAsset("path/to/your/local/my_ml_dataset.csv")
)

# Optionally, if needed, create a Bigtable instance and table for structured data
# Uncomment the code below if you wish to use Bigtable in conjunction with GCS
'''
ml_bigtable_instance = gcp.bigtable.Instance("mlBigTableInstance",
    instance_type="PRODUCTION",
    display_name="ML BigTable Instance"
)

ml_bigtable_table = gcp.bigtable.Table("mlBigTable",
    instance_name=ml_bigtable_instance.name,
    family="ml-data"
)
'''

# Export the URL of the GCS bucket to access it later
pulumi.export('bucket_url', pulumi.Output.concat("gs://", ml_data_bucket.name))

# Export the GCS object URL for direct access to the uploaded dataset
pulumi.export('dataset_object_url', pulumi.Output.concat("https://storage.googleapis.com/", ml_data_bucket.name, "/", ml_dataset_object.name))
```

This Pulumi program does the following:
- It declares a GCS bucket with a unique name and enables versioning on it. This ensures that every change to an object within the bucket keeps the older versions intact.
- It then creates a `BucketObject` which represents your ML dataset. The `source` parameter references a local file, but you can adjust it as needed to point to your data file.
- Optionally, if you uncomment the Bigtable section, it creates a scalable, high-throughput Bigtable instance and a table within it. This can be useful if you are handling high-velocity data or require fast reads and writes.
- Finally, it exports URLs for the bucket and the stored dataset object. You can use these URLs to access your data storage and versioned data within your ML applications or services.

To run this program, save it into a file `__main__.py`, navigate to the directory of the file in your terminal and then run `pulumi up`. Pulumi will perform the actions declared in the code, provisioning the necessary GCP resources in your cloud account.