Storing Metadata for Machine Learning Models in GCP SQL

Question

Pulumi · Accepted Answer

Storing metadata for machine learning models is an important part of managing the machine learning lifecycle. Metadata can include anything from the version of the model, the parameters it was trained with, performance metrics, to the dataset it was trained on. Google Cloud SQL is a fully-managed database service that makes it easy to set up and maintain relational databases on Google Cloud Platform (GCP). Using Cloud SQL, you can create a structured and queryable repository for your machine learning metadata.

In this program, we will create a Cloud SQL instance, a database within that instance, and a user with access to that database. We will use Google Cloud SQL for MySQL as our database engine, but you can also use PostgreSQL or SQL Server, depending on your preference.

Below is a Pulumi program written in Python that provisions these resources in GCP:

1. **Cloud SQL Instance**: This is the basic building block of Cloud SQL that acts as a MySQL server in this case.
2. **Database**: A logical database within the Cloud SQL instance to hold the metadata tables.
3. **User**: A user account with permissions to access and modify the database.

Here is the program:

```python
import pulumi
import pulumi_gcp as gcp

# Configuration
instance_name = 'ml-metadata-instance'
database_name = 'ml_metadata_db'
user_name = 'ml_user'
user_password = pulumi.Config().require_secret('sql_user_password')  # A secure way to handle passwords

# Create a Cloud SQL instance
sql_instance = gcp.sql.DatabaseInstance('sql-instance',
    name=instance_name,
    database_version='MYSQL_5_7',
    settings=gcp.sql.DatabaseInstanceSettingsArgs(  # Set the settings for the instance
        tier='db-f1-micro',  # Choose the machine type based on your needs
    ))

# Create a SQL database for storing ML metadata
sql_database = gcp.sql.Database('sql-database',
    name=database_name,
    instance=sql_instance.name)

# Create a SQL user that will have access to the SQL database
sql_user = gcp.sql.User('sql-user',
    name=user_name,
    instance=sql_instance.name,
    password=user_password)

# Export the instance address and database name
pulumi.export('sql_instance_address', sql_instance.private_ip_addresses[0])
pulumi.export('database_name', sql_database.name)

```

Before running this code, make sure to create a Pulumi configuration key for the SQL user password. You can do this by running `pulumi config set sql_user_password --secret` in your project directory.

The program starts by importing the required modules and setting up the configuration variables for the names of the SQL instance, database, and the user. Then, it defines the `sql_instance` resource using the `DatabaseInstance` component from pulumi_gcp. We specify the `MYSQL_5_7` version and the instance settings, including the machine tier.

Next, we create a `sql_database` resource for storing metadata within the created Cloud SQL instance. It's important to ensure that the `instance` parameter of `sql_database` matches the `name` of the `sql_instance`.

Finally, we create a `sql_user` resource with the necessary credentials to connect to the Cloud SQL database. The password is securely handled through Pulumi's config object to prevent sensitive information from being written directly in code.

We finish the program by exporting two key pieces of information: the private IP address of the Cloud SQL instance and the name of the database. These can be used by applications or administrators to connect to the database to store and retrieve ML model metadata.

After running this program with Pulumi, you will have a cloud-based relational database ready to manage your machine learning metadata. You can extend the database's schema based on the specific metadata you need to store for your ML models.