Skip to content

DeploymentConfig

relationalai.config.config_deployment_fields

Configure how a model is deployed: schema, schedules, and outputs.

This is the single section describing deployment behavior. It carries the deploy-specific scalars (schema, auto_deploy), the named refresh schedules, and the outputs configuration (default reasoner / materialization / schedule plus per-output overrides).

deployment:
schema: analytics
auto_deploy: true
schedules:
standard:
interval_s: 60
fast_path:
interval_s: 10
outputs:
type: table
schedule: standard
overrides:
- objects: [recursive_closure]
reasoner: logic
schedule: fast_path
DeploymentConfig.schema_: (str, optional)

Install schema for model-generated SQL views and tables (config key is schema).

DeploymentConfig.role: (str, optional)

Snowflake role to use when deploying the model.

DeploymentConfig.auto_deploy: bool

Automatically deploy the model before running queries.

DeploymentConfig.schedules: dict[str, ScheduleConfig]

Named refresh cadences. Each key is the schedule name (letters, digits and underscores; must start with a letter). Outputs attach to a schedule via outputs.

DeploymentConfig.outputs: OutputsConfig

Default reasoner / materialization / schedule for outputs, plus per-output overrides.

DeploymentConfig.max_parallel_exports: (int, optional)

Maximum number of LQP exports that run in parallel during a refresh (Snowflake only). If unset, the value is taken from the active warehouse’s MAX_CONCURRENCY_LEVEL, falling back to 8.

DeploymentConfig.suspend_after_mins: (int, optional)

Convenience method for development (Snowflake only). If set, the refresh procedure auto-suspends all refresh tasks in the meta schema once this many minutes have elapsed since the initial deploy start time. Intended to bound development costs; leave unset in production.

DeploymentConfigpydantic.BaseModel