ExperimentConfig
Defines the database and schema that experiment metrics, experiment metadata and registered models will be saved.
⚠️ Note:
- During each training run (a single call to
.fit()), the model with the best validation metric across all epochs is saved. - Ensure the native app has the necessary permissions to the specified database and schema.
-- grant access to resources needed for snowflake experiment tracking
GRANT USAGE ON DATABASE <DATABASE> TO APPLICATION RELATIONALAI;GRANT USAGE ON SCHEMA <DATABASE>.<SCHEMA> TO APPLICATION RELATIONALAI;GRANT CREATE EXPERIMENT ON SCHEMA <DATABASE>.<SCHEMA> TO APPLICATION RELATIONALAI;Parameters
Section titled “Parameters”| Parameter | Type | Description |
|---|---|---|
Database | str | Database to save the experiment metadata. |
Schema | str | Schema in database to save experiment metadata. |
Returns
Section titled “Returns”An instance of the ExperimentConfig class.
Example
Section titled “Example”from relationalai_gnns import ExperimentConfig
# Create an instance of ExperimentConfig; artifacts will be saved in "database_name.schema_name"experiment_cfg = ExperimentConfig(database="database_name", schema="schema_name")Methods
Section titled “Methods”to_dict()
Section titled “to_dict()”Returns a dictionary representation of the ExperimentConfig instance.
Example:
experiment_cfg.to_dict()