deploy

TimeSeriesFoundationModel.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', 'serverless'] = 'realtime', inference_config: dict[str, Any] | None = None, **backend_kwargs) → TimeSeriesEndpoint

Deploy model to an inference endpoint.

Parameters:

• instance_type (str | None, default = None) – Instance type for the endpoint. Defaults to the model registry value. Must be None when inference_mode="serverless".

• 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", "serverless"], default = "realtime") – Endpoint type. "serverless" provisions a SageMaker Serverless Inference endpoint (no instance management, scales to zero).

• inference_config (dict[str, Any] | None, default = None) – Serverless settings (memory_size_in_mb, max_concurrency, provisioned_concurrency).

• **backend_kwargs (Any) –

  Additional SageMaker arguments:

  • initial_instance_count: Number of instances for the endpoint. Defaults to 1. Ignored when inference_mode="serverless".

  • 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, or the serverless settings.

• CreateEndpoint: launches the endpoint.

The endpoint is billed until TimeSeriesEndpoint.delete_endpoint() deletes it.