Configure a model

Model configuration controls how PyRel loads your model and how it treats properties you have not declared. These settings live in the model section of your configuration. For deployment, output, and op log settings, see Configure deployment. This guide shows how to set them, and how to inspect the values PyRel actually used.

Prerequisites

• You have access to a Snowflake account with the RelationalAI Native App installed. If you are unsure, contact your Snowflake administrator.
• You have a working PyRel installation. See Install and run PyRel for instructions.

Choose which setting to change

Use this table to find the setting for what you want to change:

Setting	Use it when
model.implicit_properties	You want accessing an undeclared property to raise an error instead of creating it, for example before deploying to production.
model.path	You are deploying a model and want to point PyRel at its entry point script or directory.
deployment.public_schema	You want to choose the Snowflake schema and role a model deploys to.
deployment.identity.internal_schema	You want to choose where PyRel stores the deployment’s own internal objects and metadata.
deployment.schedules	You want to control when a deployed model’s outputs refresh after deployment.
deployment.materialized	You want to choose whether the data behind deployed outputs is stored in Snowflake tables.
deployment.incremental	You want the data behind deployed outputs stored in Snowflake dynamic tables.
deployment.overrides	You want per-output settings or access policies for deployed outputs.
deployment.tags	You want your own Snowflake object tags on everything a deploy creates, for cost attribution, classification, or tag-based policies.
deployment.preserve_column_case	You want deployed Snowflake columns to preserve the capitalization of model field names.
oplog.enabled	You want to override or explicitly declare whether rai models deploy records change history for the shared-model synchronization workflow.

Require declared properties

By default, PyRel creates a property the first time you access it, even if you never declared it. Set the model.implicit_properties configuration option to false to require every property to be declared before use, so that accessing an undeclared property raises an error. This check happens when Python builds the model, including when deployment loads your model entry point.

Tip

Requiring declared properties catches misspelled property names early, which is especially useful before you deploy a production model. With the default setting, PyRel can warn when a new property name resembles an existing one, but still creates the property. Setting implicit_properties to false makes unknown property access an error rather than relying on these warnings.

raiconfig.yaml

1. Set model.implicit_properties in raiconfig.yaml

   connections:
     # ...
   
   model:
     implicit_properties: false

2. Inspect the parsed config

   Note

   This check assumes your existing raiconfig.yaml already includes a valid connection configuration. If you define more than one connection, set default_connection so Model uses the intended config. See Configure Snowflake authentication for details on connection configuration.

   from relationalai.semantics import Model
   
   m = Model("MyModel")
   
   print(m.config.model.implicit_properties)

   A printed value of False confirms that PyRel loaded the implicit_properties setting from your config.

Programmatic

Set model.implicit_properties with ModelConfig or a plain Python dict. These examples assume file discovery already provides a valid connection configuration. Use one of the following approaches:

1. Set the value with a typed config class

   from relationalai.config import ModelConfig, create_config
   from relationalai.semantics import Model
   
   cfg = create_config(model=ModelConfig(implicit_properties=False))
   m = Model("MyModel", config=cfg)
   
   print(m.config.model.implicit_properties)

2. Set the value with a dict

   from relationalai.config import create_config
   from relationalai.semantics import Model
   
   cfg = create_config(model={"implicit_properties": False})
   m = Model("MyModel", config=cfg)
   
   print(m.config.model.implicit_properties)

Each example should print False. This confirms the configuration value, not that every property in your model has been declared. If the value differs, check which configuration file or programmatic config you passed to Model. If building your model then raises an undeclared-property error, correct the property name or declare it before use.

Related resources:

• Create configuration in code
• ModelConfig API reference

Set the model entry point

model.path points PyRel at the script or Python package directory that defines your model, for example model.py. It has no default (None).

Deployment and related tooling use model.path to locate your model. For example, rai models deploy deploys the model at model.path when you do not pass an explicit --path option to the command. Setting model.path does not import the entry point when you create a Model instance.

raiconfig.yaml

1. Set model.path in raiconfig.yaml

   connections:
     # ...
   
   model:
     path: model.py

2. Inspect the parsed config

   Note

   This check assumes your existing raiconfig.yaml already includes a valid connection configuration. If you define more than one connection, set default_connection so Model uses the intended config. See Configure Snowflake authentication for details on connection configuration.

   from relationalai.semantics import Model
   
   m = Model("MyModel")
   
   print(m.config.model.path)

   A printed value of model.py confirms that PyRel loaded the path setting from your config.

Programmatic

Set model.path with ModelConfig or a plain Python dict. These examples assume file discovery already provides a valid connection configuration. Use one of the following approaches:

1. Set the value with a typed config class

   from relationalai.config import ModelConfig, create_config
   from relationalai.semantics import Model
   
   cfg = create_config(model=ModelConfig(path="model.py"))
   m = Model("MyModel", config=cfg)
   
   print(m.config.model.path)

2. Set the value with a dict

   from relationalai.config import create_config
   from relationalai.semantics import Model
   
   cfg = create_config(model={"path": "model.py"})
   m = Model("MyModel", config=cfg)
   
   print(m.config.model.path)

Each example should print model.py. This confirms the configuration value, not that the entry point exists or loads successfully. If the value differs, check which configuration file or programmatic config you passed to Model. See Deploy a model to load and deploy the entry point.

Related resources:

• Create configuration in code
• ModelConfig API reference

Set the deployment target is now covered in Configure deployment.

Configure object name prefixes and suffixes is now covered in Configure deployment.

Configure op log recording is now covered in Configure deployment.

Use a standard op log table for a transient deployment target is now covered in Configure deployment.

Schedule output refreshes is now covered in Configure deployment.

Define a schedule is now covered in Configure deployment.

Assign outputs to schedules is now covered in Configure deployment.

Skip the deploy-time refresh is now covered in Configure deployment.

Auto-suspend test schedules is now covered in Configure deployment.

Configure deployed outputs is now covered in Configure deployment.

Choose how outputs are materialized is now covered in Configure deployment.

Configure access policies is now covered in Configure deployment.

Apply a row access policy to deployed outputs is now covered in Configure deployment.

Apply a projection policy to output columns is now covered in Configure deployment.

Apply a masking policy to output columns is now covered in Configure deployment.

Combine output policies is now covered in Configure deployment.

Check the result with consumer roles is now covered in Configure deployment.

Recover from unavailable policies is now covered in Configure deployment.

Tag deployed Snowflake objects is now covered in Configure deployment.

Preserve output column case is now covered in Configure deployment.