fit

TimeSeriesCloudPredictor.fit(train_data: str | Path | DataFrame | None = None, *, predictor_init_args: Dict[str, Any], predictor_fit_args: Dict[str, Any] | None = None, tuning_data: str | Path | DataFrame | None = None, static_features: str | Path | DataFrame | None = None, id_column: str = 'item_id', timestamp_column: str = 'timestamp', framework_version: str = 'latest', job_name: str | None = None, instance_type: str = 'ml.m5.2xlarge', instance_count: int = 1, volume_size: int = 100, custom_image_uri: str | None = None, wait: bool = True, backend_kwargs: Dict | None = None, known_covariates: str | Path | DataFrame | None = None) → TimeSeriesCloudPredictor[source]

Fit the predictor in a SageMaker training job.

Parameters:

  • train_data (Union[str, pathlib.Path, pd.DataFrame]) – Training time series in long format, as a DataFrame or local/S3 path to a data file. See the TimeSeriesPredictor.fit docs for the expected format.

  • predictor_init_args (dict) – Arguments forwarded to TimeSeriesPredictor(). See the TimeSeriesPredictor docs for available options (e.g. target, prediction_length, freq, eval_metric, quantile_levels, known_covariates_names).

  • predictor_fit_args (Optional[dict], default = None) –

    Additional fit args forwarded to TimeSeriesPredictor.fit(). See the TimeSeriesPredictor.fit docs for available options. Must NOT contain train_data or tuning_data — pass those as explicit arguments above.

  • tuning_data (Optional[Union[str, pathlib.Path, pd.DataFrame]], default = None) – Optional tuning data in long format, as a DataFrame or local/S3 path to a data file.

  • known_covariates (Optional[Union[str, pathlib.Path, pd.DataFrame]], default = None) – Values of the known covariates. Must be provided if known_covariates_names was specified in predictor_init_args.

  • static_features (Optional[Union[str, pathlib.Path, pd.DataFrame]], default = None) – Static (time-independent) features describing each individual time series.

  • id_column (str, default = "item_id") – Name of the column with the unique identifier of each time series (item).

  • timestamp_column (str, default = "timestamp") – Name of the column with the observation timestamps.

  • 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 instances used to fit the predictor.

  • volume_size (int, default = 100) – 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).

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

  • 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:

TimeSeriesCloudPredictor object. Returns self.