1. Metadata Store in PostgreSQL for ML Experiment Tracking


    To set up a metadata store in PostgreSQL for tracking machine learning experiments, you would typically create a PostgreSQL database and define the required schemas, tables, and potentially views or functions to manage and query your experimental metadata. This setup would involve the following actions:

    1. Deploying a PostgreSQL server (or using a managed PostgreSQL service).
    2. Setting up a database within the PostgreSQL server specifically for tracking metadata.
    3. Creating schema definitions for tables that store experiments, metrics, parameters, and any other relevant data.
    4. Potentially defining views or custom functions to ease the querying and aggregation of data.

    In the context of Pulumi, you can achieve this by declaring resources that represent each of these actions within your Pulumi program. Below is a program written in Python that demonstrates how you could use Pulumi to automate the setup of a PostgreSQL database with the basic schema needed for ML experiment tracking:

    import pulumi import pulumi_postgresql as postgresql # Creating a new PostgreSQL server, this example assumes you are using a cloud provider like AWS, Azure, or GCP. # If you are using a managed service (e.g., AWS RDS, Azure Database for PostgreSQL, or GCP Cloud SQL), # you would need the respective Pulumi provider to create an instance of the service. # For illustrative purposes, this example assumes a PostgreSQL server is already running. # Creating a new database for ML metadata tracking. ml_metadata_db = postgresql.Database("ml_metadata_db", name="ml_metadata", # Assuming a PostgreSQL role 'db_owner' exists, the role should have privileges to create schemas and tables in the new database. owner="db_owner" ) # Define a schema within the database for organizing ML experiment tables. ml_schema = postgresql.Schema("ml_schema", name="machine_learning", database=ml_metadata_db.name, owner="db_owner" ) # Define a table for storing experiments. experiments_table = postgresql.Table("experiments_table", name="experiments", database=ml_metadata_db.name, schema=ml_schema.name, columns=[ # Experiment ID (Primary Key), Change the data types as needed. postgresql.TableColumnArgs(name="id", type="serial", nullable=False), postgresql.TableColumnArgs(name="name", type="varchar", nullable=False), postgresql.TableColumnArgs(name="start_time", type="timestamp", nullable=False), postgresql.TableColumnArgs(name="end_time", type="timestamp"), # Add more columns as necessary for your experiment tracking. ], primary_keys=["id"] ) # Define a table for metrics linked to experiments. metrics_table = postgresql.Table("metrics_table", name="metrics", database=ml_metadata_db.name, schema=ml_schema.name, columns=[ # Metric ID (Primary Key). postgresql.TableColumnArgs(name="id", type="serial", nullable=False), # Experiment ID (Foreign Key). postgresql.TableColumnArgs(name="experiment_id", type="integer", nullable=False), postgresql.TableColumnArgs(name="metric_name", type="varchar", nullable=False), postgresql.TableColumnArgs(name="value", type="float", nullable=False), # This is an explicit foreign key relation to the experiments table. # It ensures that each metric is associated with a valid experiment. postgresql.TableColumnArgs(name="experiment_id", type="integer", nullable=False, references=postgresql.TableColumnReferenceArgs( schema=ml_schema.name, table=experiments_table.name, column="id" )) ], primary_keys=["id"] ) # pulumi.export is used to display the output once the pulumi program is executed. # For example, exporting the database name. pulumi.export("ml_metadata_db_name", ml_metadata_db.name)

    This Pulumi program accomplishes the following:

    • It sets up a database called ml_metadata that serves as the central repository for your ML experiment metadata.
    • It creates a schema machine_learning inside the database to organize your ML-related tables.
    • It declares a table experiments within the schema to store details about each experiment.
    • It also sets up a metrics table intended to store metrics for each experiment, with a foreign key reference to the experiments table.
    • It exports the name of the created database as an output, which you can then use to connect to the database and begin tracking experiments.

    When you run this Pulumi program, it will deploy these PostgreSQL resources in the order specified, taking care of the dependencies between them automatically. This setup assumes that you already have a PostgreSQL server running and accessible, and it does not address the authentication or networking aspects which would be specific to your setup.

    Remember that setting up your machine learning metadata store may necessitate additional tables or more complex relationships, indexes, and potentially stored procedures or triggers depending on your tracking requirements. The provided program is a starting point and can be extended according to your needs.