Wraps a backend Model object.
Model( client: Optional[wallaroo.client.Client], data: Dict[str, Any], standalone=False)
Each object requires:
- a GraphQL client - in order to fill its missing members dynamically
- an initial data blob - typically from unserialized JSON, contains at
- least the data for required members (typically the object's primary key) and optionally other data members.
@staticmethoddef as_standalone(name: str, version: str, file_name: str) -> wallaroo.model.Model:
Creates a Model intended for use in generating standalone configurations
def configure( self, runtime: Optional[str] = None, tensor_fields: List[str] = None, filter_threshold: float = None, input_schema: Optional[pyarrow.lib.Schema] = None, output_schema: Optional[pyarrow.lib.Schema] = None) -> wallaroo.model.Model:
def deploy( self, pipeline_name: str, deployment_config: Optional[wallaroo.deployment_config.DeploymentConfig] = None) -> wallaroo.pipeline.Pipeline:
Convenience function to quickly deploy a Model. It will configure the model, create a pipeline with a single model step, deploy it, and return the pipeline.
Typically, the configure() method is used to configure a model prior to deploying it. However, if a default configuration is sufficient, this function can be used to quickly deploy with said default configuration.
The filename this Model was generated from needs to have a recognizable file extension so that the runtime can be inferred. Currently, this is:
.onnx-> ONNX runtime
- str deployment_name: Name of the deployment to create. Must be unique across all deployments. Deployment names must be ASCII alpha-numeric characters plus dash (-) only.
Wraps a list of Models for display in a display-aware environment like Jupyter.