deploy¶
- TabularFoundationModel.deploy(*, instance_type: str | None = None, endpoint_name: str | None = None, hyperparameters: dict[str, Any] | None = None, framework_version: str = '1.6', wait: bool = True, inference_mode: Literal['realtime'] = 'realtime', inference_config: dict[str, Any] | None = None, backend_overrides: dict[str, dict[str, Any]] | None = None, **kwargs: Unpack[DeployKwargs]) TabularEndpoint[source]¶
Deploy the model to a real-time endpoint.
Each request to the endpoint sends the labeled
train_dataalong with the rows to predict. Serverless endpoints are not supported.- Parameters:
instance_type (str | None, default = None) – Instance type for the endpoint. Defaults to the model registry value.
endpoint_name (str | None, default = None) – Name of the endpoint. If None, a unique name is generated.
hyperparameters (dict[str, Any] | None, default = None) – Model hyperparameters for inference. Overrides values passed to the constructor.
framework_version (str, optional) – AutoGluon version, e.g. “1.6”. Uses the official AutoGluon DLC image for this version.
wait (bool, default = True) – Whether to block until the endpoint is ready.
inference_mode (Literal["realtime"], default = "realtime") – Endpoint type. Only
"realtime"is supported.inference_config (dict[str, Any] | None, default = None) – Not supported; must be None.
- Returns:
TabularEndpoint – Handle to the deployed endpoint.
- Other Parameters:
backend_overrides (dict[str, dict[str, Any]] | None, default = None) – Raw SageMaker request fields for settings without a dedicated argument.
Keys: request names from the SageMaker API section below.
Values: request fields in PascalCase, as in the SageMaker API and boto3. Deep-merged over the request built by AutoGluon-Cloud; lists and other non-dict values replace the generated ones.
Example:
{"ProductionVariant": {"ModelDataDownloadTimeoutInSeconds": 1200}}
initial_instance_count (int, default = 1) – Number of instances for the endpoint.
volume_size (int | None, default = None) – Size in GB of the EBS volume to use for the endpoint. Ignored for GPU instances.
custom_image_uri (str | None, default = None) – Custom inference container image URI. If set,
framework_versionis ignored.
SageMaker API
CreateModel: registers the model artifact and inference image as a SageMaker model.
CreateEndpointConfig: defines the endpoint’s single ProductionVariant: instance type and count.
CreateEndpoint: launches the endpoint.
The endpoint is billed until
TabularEndpoint.delete_endpoint()deletes it.