fit

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

Fit the predictor in a SageMaker training job.

Parameters:

  • train_data (Union[str, pathlib.Path, pd.DataFrame]) – Training data, as a DataFrame or local/S3 path to a data file.

  • tuning_data (Optional[Union[str, pathlib.Path, pd.DataFrame]], default = None) – Optional tuning data.

  • predictor_init_args (dict) – Init args for the predictor.

  • predictor_fit_args (Optional[dict], default = None) – Additional fit args forwarded to the underlying predictor’s fit(). Must NOT contain train_data or tuning_data — pass those as explicit arguments above.

  • 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 (Union[int, str], default = "auto") – Number of instances used to fit the predictor. If “auto”, the backend decides the instance count.

  • volume_size (int, default = 256) – Size in GB of the EBS volume to use for storing input data during training. 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/v2/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/v2/api/training/estimators.html#sagemaker.estimator.Estimator.fit for all options

Return type:

CloudPredictor object. Returns self.