deploy

TabularCloudPredictor.deploy(*, predictor_path: str | None = None, endpoint_name: str | None = None, framework_version: str | None = None, instance_type: str | None = None, wait: bool = True, inference_mode: Literal['realtime', 'serverless'] = 'realtime', inference_config: dict[str, Any] | None = None, backend_overrides: dict[str, dict[str, Any]] | None = None, **kwargs: Unpack[DeployKwargs]) → EndpointT

Deploy a predictor to a real-time inference endpoint.

Returns a handle to the endpoint; call its predict() for low-latency inference, then delete_endpoint() to tear it down.

Parameters:

  • predictor_path (str | None, default = None) – Local or S3 path of the predictor tarball to deploy. If None, deploys the predictor trained by fit().

  • endpoint_name (str | None, default = None) – Name of the endpoint. If None, a unique name with prefix ag-cloud-<predictor_type> is generated.

  • framework_version (str | None, default = None) – AutoGluon version, e.g. "1.6". Inference uses the official AutoGluon DLC image for this version. Defaults to the version used by fit(). Ignored if custom_image_uri is set.

  • instance_type (str | None, default = None) – Instance type of the endpoint. Defaults to ml.m5.2xlarge. Must be None when inference_mode="serverless".

  • wait (bool, default = True) – Whether to block until the endpoint is in service.

  • inference_mode ({"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).

Returns:

TabularEndpoint | TimeSeriesEndpoint | MultiModalEndpoint – Handle to the deployed endpoint, matching the predictor type.

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) – Initial number of endpoint instances. Ignored when inference_mode="serverless".

  • volume_size (int | None, default = None) – Size in GB of the endpoint’s EBS volume. Ignored for GPU instances (ml.g* / ml.p*).

  • custom_image_uri (str | None, default = None) – Custom inference container image URI. If set, framework_version is ignored.

SageMaker API

The endpoint is billed until delete_endpoint() of the returned endpoint deletes it.