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 = '1.6', job_name: str | None = None, instance_type: str = 'ml.m5.2xlarge', instance_count: int | str = 'auto', volume_size: int = 100, custom_image_uri: str | None = None, timeout: int = 86400, wait: bool = True, backend_overrides: dict[str, dict[str, Any]] | None = None, **kwargs) → CloudPredictor

Fit the predictor in a SageMaker training job.

Parameters:

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

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

  • predictor_init_args (dict) – Arguments forwarded to the underlying predictor’s constructor, e.g. {"label": "target"}.

  • predictor_fit_args (dict | None, 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.

  • leaderboard (bool, default = True) – Whether to save the leaderboard in the output artifact, so that leaderboard() can read it.

  • 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.

  • job_name (str | None, default = None) – Name of the training job. If None, a unique name with prefix ag-cloud-<predictor_type> is generated.

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

  • instance_count (int | str, default = "auto") – Number of training instances. Only single-instance training is supported, so this is always 1.

  • 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 = 24*60*60) – Maximum training job runtime in seconds.

  • 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.

  • 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}}}

Returns:

CloudPredictor – The fitted predictor (self).

SageMaker API

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