TabularCloudPredictor

class autogluon.cloud.TabularCloudPredictor(local_output_path: str | None = None, cloud_output_path: str | None = None, backend: str = 'sagemaker', role: str | None = None, verbosity: int = 2)

Train and deploy AutoGluon tabular models (classification and regression) on Amazon SageMaker.

Wraps autogluon.tabular.TabularPredictor (docs) and runs fit, predict, and endpoint deployment as managed SageMaker jobs.

Parameters:

• local_output_path (str | None, default = None) – Local directory for the saved predictor, downloaded artifacts, and intermediate files. If None, a timestamped folder AutogluonCloudPredictor/ag-<timestamp> is created in the working directory. Reusing the same path for two predictors overwrites the files of the first one.

• cloud_output_path (str | None, default = None) –

  S3 location where intermediate artifacts and trained models are stored. Accepts:

  • s3://bucket — a unique timestamped subfolder ag-<timestamp> is appended, so each call gets its own folder and repeated runs don’t overwrite each other.

  • s3://bucket/prefix — used verbatim. Re-running with the same prefix will overwrite previously written artifacts.

  • None (default) — use the bucket saved in ~/.autogluon/cloud.yaml (set by autogluon.cloud.bootstrap() / autogluon.cloud.register()) and append a timestamped subfolder. Raises if no bucket is configured.

• backend (str, default = "sagemaker") – Cloud backend to use. Currently only "sagemaker" is supported.

• role (str | None, default = None) – ARN of the SageMaker execution role used to run training and inference jobs. If None, falls back to role_arn in ~/.autogluon/cloud.yaml (set by autogluon.cloud.bootstrap() / autogluon.cloud.register()), and finally to the role of the current AWS identity.

• verbosity (int, default = 2) – Logging verbosity from 0 (errors only) to 4 (debug).

Methods

attach_job	Attach to an existing SageMaker training job, e.g. after the local process that launched it crashed.
deploy	Deploy a predictor to a real-time inference endpoint.
download_trained_predictor	Download and extract the trained predictor.
fit	Fit the predictor in a SageMaker training job.
fit_predict	Fit and predict in a single SageMaker training job.
fit_predict_proba	Fit and predict probabilities in a single SageMaker training job.
get_batch_inference_job_info	Get info about a batch inference job launched by this predictor.
get_batch_inference_job_status	Get the status of a batch inference job, e.g. after calling predict(wait=False).
get_fit_job_output_path	Get the S3 path of the trained predictor artifact (model.tar.gz).
get_fit_job_status	Get the status of the training job, e.g. after calling fit(wait=False).
get_fit_predict_proba_results	Retrieve the predictions and probabilities of a completed fit_predict() or fit_predict_proba() job.
get_fit_predict_results	Retrieve the predictions of a completed fit_predict() or fit_predict_proba() job.
info	Return a summary of the predictor: output paths, the training job, batch inference jobs, and the endpoint.
leaderboard	Return the leaderboard of models trained by the completed fit() job.
load	Load a predictor saved with save().
predict	Predict with a SageMaker batch transform job.
predict_proba	Predict class probabilities with a SageMaker batch transform job.
save	Save the predictor to local_output_path, so it can be restored later with load().
to_local_predictor	Download the trained predictor and load it as a local AutoGluon predictor.

Attributes

backend_map
endpoint_name	Name of the most recent endpoint deployed by this predictor, or None.
is_fit	Whether the training job has completed successfully.
predictor_file_name
predictor_type	Type of the underlying AutoGluon predictor.