deploy

TabularFoundationModel.deploy(instance_type: str | None = None, endpoint_name: str | None = None, hyperparameters: dict[str, Any] | None = None, framework_version: str = '1.6', custom_image_uri: str | None = None, wait: bool = True, inference_mode: Literal['realtime'] = 'realtime', inference_config: dict[str, Any] | None = None, **backend_kwargs) → TabularEndpoint

Deploy the tabular foundation model to an inference endpoint.

The returned endpoint accepts both labeled train_data and the rows to predict. It fits a request-scoped TabularPredictor before producing predictions.

Only real-time inference is supported. Tabular foundation models such as Mitra require a provisioned instance and cannot be deployed with SageMaker Serverless Inference.

Parameters:

• instance_type (str | None, default = None) – Instance type for the endpoint. Defaults to the model registry value.

• endpoint_name (str | None, default = None) – Custom endpoint name. If None, will auto-generate a unique name.

• hyperparameters (dict[str, Any] | None, default = None) – Model hyperparameters for inference. Overrides values passed to the constructor.

• framework_version (str, default = "1.6") – AutoGluon version, e.g. “1.6”. Uses the official AutoGluon DLC image for this version.

• custom_image_uri (str | None, default = None) – Custom Docker image URI for the inference container.

• 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.

• **backend_kwargs (Any) –

  Additional SageMaker arguments:

  • initial_instance_count: Number of instances for the endpoint. Defaults to 1.

  • volume_size: Size in GB of the EBS volume to use for the endpoint. Ignored for GPU instances.

  • backend_overrides: 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}}

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.