TabularCloudPredictor.deploy#

TabularCloudPredictor.deploy(predictor_path: Optional[str] = None, endpoint_name: Optional[str] = None, framework_version: str = 'latest', instance_type: str = 'ml.m5.2xlarge', initial_instance_count: int = 1, custom_image_uri: Optional[str] = None, wait: bool = True, backend_kwargs: Optional[Dict] = None) → None#

Deploy a predictor to an endpoint, which can be used to do real-time inference later.

Parameters

  • predictor_path (str) – Path to the predictor tarball you want to deploy. Path can be both a local path or a S3 location. If None, will deploy the most recent trained predictor trained with fit().

  • endpoint_name (str) – The endpoint name to use for the deployment. If None, CloudPredictor will create one with prefix ag-cloudpredictor

  • framework_version (str, default = latest) – Inference container version of autogluon. If latest, will use the latest available container version. If provided a specific version, will use this version. If custom_image_uri is set, this argument will be ignored.

  • instance_type (str, default = 'ml.m5.2xlarge') – Instance to be deployed for the endpoint

  • initial_instance_count (int, default = 1,) – Initial number of instances to be deployed for the endpoint

  • wait (Bool, default = True,) – Whether to wait for the endpoint to be deployed. To be noticed, the function won’t return immediately because there are some preparations needed prior deployment.

  • backend_kwargs (dict, default = None) –

    Any extra arguments needed to pass to the underneath backend. For SageMaker backend, valid keys are:

    1. model_kwargs: dict, default = dict()

      Any extra arguments needed to initialize Sagemaker Model Please refer to https://sagemaker.readthedocs.io/en/stable/api/inference/model.html#model for all options

    2. deploy_kwargs

      Any extra arguments needed to pass to deploy. Please refer to https://sagemaker.readthedocs.io/en/stable/api/inference/model.html#sagemaker.model.Model.deploy for all options