MultiModalCloudPredictor.fit#

MultiModalCloudPredictor.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: int = 1, volume_size: int = 100, custom_image_uri: Optional[str] = None, 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.

  • volumes_size (int, default = 30) – Size in GB of the EBS volume to use for storing input data during training (default: 30). Must be large enough to store training data if File Mode is used (which is the default).

  • 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

Return type

CloudPredictor object. Returns self.