TabularCloudPredictor.fit#

TabularCloudPredictor.fit(*, predictor_init_args: Dict[str, Any], predictor_fit_args: Dict[str, Any], image_column: Optional[str] = None, leaderboard: bool = True, framework_version: str = 'latest', job_name: Optional[str] = None, instance_type: str = 'ml.m5.2xlarge', instance_count: Union[int, str] = 'auto', volume_size: int = 256, custom_image_uri: Optional[str] = None, timeout: int = 86400, wait: bool = True, backend_kwargs: Optional[Dict] = None) → CloudPredictor#

Fit the predictor with the backend.

Parameters

  • predictor_init_args (dict) – Init args for the predictor

  • predictor_fit_args (dict) – Fit args for the predictor

  • image_column (str, default = None) – The column name in the training/tuning data that contains the image paths. The image paths MUST be absolute paths to you local system.

  • leaderboard (bool, default = True) – Whether to include the leaderboard in the output artifact

  • framework_version (str, default = latest) – Training 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.

  • job_name (str, default = None) – Name of the launched training job. If None, CloudPredictor will create one with prefix ag-cloudpredictor

  • instance_type (str, default = 'ml.m5.2xlarge') – Instance type the predictor will be trained on with SageMaker.

  • instance_count (int, default = 1) – Number of instance used to fit the predictor. If not specified, will decide by the backend

  • volumes_size (int, default = 256) – Size in GB of the EBS volume to use for storing input data during training (default: 256). Must be large enough to store training data if File Mode is used (which is the default).

  • timeout (int, default = 24*60*60) – Timeout in seconds for training. This timeout doesn’t include time for pre-processing or launching up the training job.

  • wait (bool, default = True) – Whether the call should wait until the job completes To be noticed, the function won’t return immediately because there are some preparations needed prior fit. Use get_fit_job_status to get job status.

  • backend_kwargs (dict, default = None) –

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

    1. autogluon_sagemaker_estimator_kwargs

      Any extra arguments needed to initialize AutoGluonSagemakerEstimator Please refer to https://sagemaker.readthedocs.io/en/stable/api/training/estimators.html#sagemaker.estimator.Estimator for all options

    2. fit_kwargs

      Any extra arguments needed to pass to fit. Please refer to https://sagemaker.readthedocs.io/en/stable/api/training/estimators.html#sagemaker.estimator.Estimator.fit for all options

    For RayAWS backend, valid keys are:
    1. custom_config: Optional[Union[str, Dict[str, Any]]] = None,

      The custom cluster configuration. Please refer to https://docs.ray.io/en/latest/cluster/vms/references/ray-cluster-configuration.html#cluster-yaml-configuration-options for details

    2. cluster_name: Optional[str] = None,

      The name of the ephemeral cluster being created. If not specified, will be auto-generated with format f”ag_ray_aws_default_{timestamp}”

    3. initialization_commands: Optional[List[str]], default = None

      The initialization commands of the ray cluster. If not specified, will contain a default ECR login command to be able to pull AG DLC image, i.e.

      • aws ecr get-login-password –region us-east-1 | docker login –username AWS –password-stdin 763104351884.dkr.ecr.us-east-1.amazonaws.com

      To learn more about initialization_commands,

      https://docs.ray.io/en/latest/cluster/vms/references/ray-cluster-configuration.html#initialization-commands

Return type

CloudPredictor object. Returns self.