If the endpoint model signature is different or you want to set extra params,
you can use transform_input_fn and transform_output_fn to apply necessary
transformations before and after the query.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param api_token: str [Optional]¶
Databricks personal access token.
If not provided, the default value is determined by
the DATABRICKS_TOKEN environment variable if present, or
an automatically generated temporary token if running inside a Databricks
notebook attached to an interactive cluster in “single user” or
“no isolation shared” mode.
param cache: Optional[bool] = None¶
param callback_manager: Optional[BaseCallbackManager] = None¶
param callbacks: Callbacks = None¶
param cluster_driver_port: Optional[str] = None¶
The port number used by the HTTP server running on the cluster driver node.
The server should listen on the driver IP address or simply 0.0.0.0 to connect.
We recommend the server using a port number between [3000, 8000].
param cluster_id: Optional[str] = None¶
ID of the cluster if connecting to a cluster driver proxy app.
If neither endpoint_name nor cluster_id is not provided and the code runs
inside a Databricks notebook attached to an interactive cluster in “single user”
or “no isolation shared” mode, the current cluster ID is used as default.
You must not set both endpoint_name and cluster_id.
param endpoint_name: Optional[str] = None¶
Name of the model serving endpoint.
You must specify the endpoint name to connect to a model serving endpoint.
You must not set both endpoint_name and cluster_id.
param host: str [Optional]¶
Databricks workspace hostname.