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, known_covariates: str | Path | DataFrame | None = None, static_features: str | Path | DataFrame | None = None, id_column: str = 'item_id', timestamp_column: str = 'timestamp', framework_version: str = '1.6', instance_type: str = 'ml.m5.2xlarge', wait: bool = True, backend_overrides: dict[str, dict[str, Any]] | None = None, **kwargs: Unpack[TrainingJobKwargs]) → Self[source]

Fit the predictor in a SageMaker training job.

Parameters:

  • train_data (str | pathlib.Path | pd.DataFrame) – Training time series in long format, as a pd.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 (dict | None, 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 (str | pathlib.Path | pd.DataFrame | None, default = None) – Optional tuning data in long format, as a pd.DataFrame or local/S3 path to a data file.

  • known_covariates (str | pathlib.Path | pd.DataFrame | None, default = None) – Values of the known covariates over the training period. Must be provided if known_covariates_names is set in predictor_init_args.

  • static_features (str | pathlib.Path | pd.DataFrame | None, 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, optional) – AutoGluon version, e.g. "1.6". Training uses the official AutoGluon DLC image for this version. Ignored if custom_image_uri is set.

  • instance_type (str, default = "ml.m5.2xlarge") – Instance type of the training job.

  • wait (bool, default = True) – Whether to block until the job completes. If False, returns once the job is launched; use get_fit_job_status() to poll it.

Returns:

TimeSeriesCloudPredictor – The fitted predictor (self).

Other Parameters:

  • backend_overrides (dict[str, dict[str, Any]] | None, default = None) – Raw SageMaker request fields for settings without a dedicated argument.

    • Keys: request names from the SageMaker API section below.

    • Values: request fields in PascalCase, as in the SageMaker API and boto3. Deep-merged over the request built by AutoGluon-Cloud; lists and other non-dict values replace the generated ones.

    • Example: {"CreateTrainingJob": {"RetryStrategy": {"MaximumRetryAttempts": 2}}}

  • job_name (str | None, default = None) – Name of the training job. Defaults to a unique name with prefix ag-cloud-timeseries.

  • volume_size (int, default = 100) – Size in GB of the EBS volume that stores the training data and model artifacts.

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

  • timeout (int, default = 86400) – Maximum training job runtime in seconds. Defaults to 24 hours.

SageMaker API

  • CreateTrainingJob: trains the predictor on instance_type and writes the artifact to cloud_output_path.