Fix docstrings

README.md

@@ -5,7 +5,7 @@
 
 ## Train and Deploy AutoGluon in the Cloud
 
-[![Latest Release](https://img.shields.io/github/v/release/autogluon/autogluon-cloud)](https://github.com/autogluon/autogluon-cloud/releases)
+[![PyPI](https://img.shields.io/pypi/v/autogluon.cloud.svg)](https://pypi.org/project/autogluon.cloud/)
 [![Python Versions](https://img.shields.io/badge/python-3.10%20%7C%203.11%20%7C%203.12-blue)](https://pypi.org/project/autogluon.cloud/)
 [![GitHub license](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](./LICENSE)
 [![Continuous Integration](https://github.com/autogluon/autogluon-cloud/actions/workflows/continuous_integration.yml/badge.svg)](https://github.com/autogluon/autogluon-cloud/actions/workflows/continuous_integration.yml)
@@ -14,12 +14,12 @@
 
 </div>
 
-AutoGluon-Cloud makes it easy to run [AutoGluon](https://auto.gluon.ai/stable/index.html) in the cloud. With a few lines of code, you can train models and run inference on [Amazon SageMaker](https://aws.amazon.com/sagemaker/) — without managing infrastructure or installing AutoGluon's heavy dependencies on your local machine.
+[AutoGluon](https://auto.gluon.ai/stable/index.html) is an open-source AutoML library that trains state-of-the-art ML models on tabular, time-series, and multimodal data with just a few lines of code. AutoGluon-Cloud takes that same API and runs it on AWS — train models and serve predictions on [Amazon SageMaker](https://aws.amazon.com/sagemaker/) without managing infrastructure or setting up a heavyweight ML environment on your local machine.
 
 It supports two workflows:
 
-- **Train AutoGluon predictors in the cloud** — the same `fit → deploy → predict` workflow as local AutoGluon, with all the heavy lifting offloaded to SageMaker.
-- **Run pretrained foundation models** — deploy state-of-the-art pretrained models like Chronos-2 for zero-shot inference, with no training required.
+- **[Train your own predictor](https://auto.gluon.ai/cloud/stable/tutorials/predictor-tabular.html)** — the same `fit → deploy → predict` workflow as local AutoGluon, with all the heavy lifting offloaded to SageMaker.
+- **[Run pretrained foundation models](https://auto.gluon.ai/cloud/stable/tutorials/foundation-model-timeseries.html)** — deploy state-of-the-art pretrained models like [Chronos-2](https://huggingface.co/amazon/chronos-2) for zero-shot inference, with no training required.
 
 ## 💾 Installation & setup
 

src/autogluon/cloud/model/foundation_model.py

@@ -47,13 +47,14 @@ class FoundationModel:
     """
     Pretrained foundation model inference on AWS.
 
-    Factory: FoundationModel("chronos-bolt-base", ...) returns the appropriate
-    task-specific subclass (TimeSeriesFoundationModel, TabularFoundationModel).
+    Factory: ``FoundationModel(model_id, ...)`` dispatches on the model's task and returns the
+    appropriate task-specific subclass (:class:`TimeSeriesFoundationModel`, ``TabularFoundationModel``).
+    Most users instantiate the subclass directly instead.
 
     Examples
     --------
-    >>> model = FoundationModel("chronos-bolt-base")
-    >>> predictions = model.predict(data, prediction_length=12)
+    >>> model = FoundationModel("chronos-2")  # returns a TimeSeriesFoundationModel
+    >>> predictions = model.predict(data, prediction_length=24)
     """
 
     _backend_map: Dict[str, str] = {}
@@ -84,7 +85,9 @@ def __init__(
         Parameters
         ----------
         model_id
-            ID of the foundation model from the model registry.
+            ID of the foundation model from the model registry. See
+            `Available models <https://auto.gluon.ai/cloud/stable/tutorials/foundation-model-timeseries.html#available-models>`_
+            in the foundation model tutorial for the list of supported values.
         cloud_output_path
             S3 location where intermediate artifacts are stored. Accepts:
 
@@ -373,7 +376,24 @@ def from_json(cls, s: str, **runtime_context: Any) -> "FoundationModel":
 
 
 class TimeSeriesFoundationModel(FoundationModel):
-    """Foundation model for time series forecasting (Chronos, etc.)."""
+    """Pretrained time series foundation model for zero-shot forecasting on AWS SageMaker.
+
+    Wraps pretrained models like `Chronos-2 <https://huggingface.co/autogluon/chronos-2>`_ and
+    Chronos-Bolt and runs prediction as a managed SageMaker job, with no training required. See
+    `the foundation model tutorial <https://auto.gluon.ai/cloud/stable/tutorials/foundation-model-timeseries.html>`_
+    for the supported ``model_id`` values and a full walkthrough.
+
+    Predictions can be produced in three modes:
+
+    * **Batch** — :meth:`predict` runs a one-off SageMaker training job and writes forecasts to S3.
+      Best for one-shot inference.
+    * **Real-time** — :meth:`deploy` provisions a real-time endpoint; call
+      :meth:`TimeSeriesEndpoint.predict` for low-latency inference, then
+      :meth:`TimeSeriesEndpoint.delete_endpoint` to tear it down.
+    * **Serverless** — :meth:`deploy` with ``inference_mode="serverless"`` provisions a SageMaker
+      Serverless Inference endpoint that scales to zero. Requires a cached model artifact (see
+      :meth:`cache_model_artifact`).
+    """
 
     _backend_map = {SAGEMAKER: TIMESERIES_SAGEMAKER}
     _predictor_type = "timeseries"

src/autogluon/cloud/predictor/cloud_predictor.py

@@ -224,10 +224,10 @@ def fit(
             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/stable/api/training/estimators.html#sagemaker.estimator.Estimator for all options
+                    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/stable/api/training/estimators.html#sagemaker.estimator.Estimator.fit for all options
+                    Please refer to https://sagemaker.readthedocs.io/en/v2/api/training/estimators.html#sagemaker.estimator.Estimator.fit for all options
             For RayAWS backend, valid keys are:
                 1. custom_config: Optional[Union[str, Dict[str, Any]]] = None,
                     The custom cluster configuration.
@@ -441,10 +441,10 @@ def deploy(
             For SageMaker backend, valid keys are:
                 1. model_kwargs: dict, default = dict()
                     Any extra arguments needed to initialize Sagemaker Model
-                    Please refer to https://sagemaker.readthedocs.io/en/stable/api/inference/model.html#model for all options
+                    Please refer to https://sagemaker.readthedocs.io/en/v2/api/inference/model.html#model for all options
                 2. deploy_kwargs
                     Any extra arguments needed to pass to deploy.
-                    Please refer to https://sagemaker.readthedocs.io/en/stable/api/inference/model.html#sagemaker.model.Model.deploy for all options
+                    Please refer to https://sagemaker.readthedocs.io/en/v2/api/inference/model.html#sagemaker.model.Model.deploy for all options
         """
         if inference_mode == "serverless" and instance_type is not None:
             raise ValueError("`instance_type` must not be set when `inference_mode='serverless'`.")
@@ -621,14 +621,14 @@ def predict(
                     If `persist` is `False`, file would first be downloaded to this path and then removed.
                 4. model_kwargs: dict, default = dict()
                     Any extra arguments needed to initialize Sagemaker Model
-                    Please refer to https://sagemaker.readthedocs.io/en/stable/api/inference/model.html#model for all options
+                    Please refer to https://sagemaker.readthedocs.io/en/v2/api/inference/model.html#model for all options
                 5. transformer_kwargs: dict
                     Any extra arguments needed to pass to transformer.
-                    Please refer to https://sagemaker.readthedocs.io/en/stable/api/inference/transformer.html#sagemaker.transformer.Transformer for all options.
+                    Please refer to https://sagemaker.readthedocs.io/en/v2/api/inference/transformer.html#sagemaker.transformer.Transformer for all options.
                 6. transform_kwargs:
                     Any extra arguments needed to pass to transform.
                     Please refer to
-                    https://sagemaker.readthedocs.io/en/stable/api/inference/transformer.html#sagemaker.transformer.Transformer.transform for all options.
+                    https://sagemaker.readthedocs.io/en/v2/api/inference/transformer.html#sagemaker.transformer.Transformer.transform for all options.
 
         Returns
         -------
@@ -716,14 +716,14 @@ def predict_proba(
                     If `persist` is `False`, file would first be downloaded to this path and then removed.
                 4. model_kwargs: dict, default = dict()
                     Any extra arguments needed to initialize Sagemaker Model
-                    Please refer to https://sagemaker.readthedocs.io/en/stable/api/inference/model.html#model for all options
+                    Please refer to https://sagemaker.readthedocs.io/en/v2/api/inference/model.html#model for all options
                 5. transformer_kwargs: dict
                     Any extra arguments needed to pass to transformer.
-                    Please refer to https://sagemaker.readthedocs.io/en/stable/api/inference/transformer.html#sagemaker.transformer.Transformer for all options.
+                    Please refer to https://sagemaker.readthedocs.io/en/v2/api/inference/transformer.html#sagemaker.transformer.Transformer for all options.
                 6. transform_kwargs:
                     Any extra arguments needed to pass to transform.
                     Please refer to
-                    https://sagemaker.readthedocs.io/en/stable/api/inference/transformer.html#sagemaker.transformer.Transformer.transform for all options.
+                    https://sagemaker.readthedocs.io/en/v2/api/inference/transformer.html#sagemaker.transformer.Transformer.transform for all options.
 
         Returns
         -------

src/autogluon/cloud/predictor/timeseries_cloud_predictor.py

@@ -108,10 +108,10 @@ def fit(
             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/stable/api/training/estimators.html#sagemaker.estimator.Estimator for all options
+                    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/stable/api/training/estimators.html#sagemaker.estimator.Estimator.fit for all options
+                    Please refer to https://sagemaker.readthedocs.io/en/v2/api/training/estimators.html#sagemaker.estimator.Estimator.fit for all options
 
         Returns
         -------
@@ -286,14 +286,14 @@ def predict(
                     If `persist` is `False`, file would first be downloaded to this path and then removed.
                 4. model_kwargs: dict, default = dict()
                     Any extra arguments needed to initialize Sagemaker Model
-                    Please refer to https://sagemaker.readthedocs.io/en/stable/api/inference/model.html#model for all options
+                    Please refer to https://sagemaker.readthedocs.io/en/v2/api/inference/model.html#model for all options
                 5. transformer_kwargs: dict
                     Any extra arguments needed to pass to transformer.
-                    Please refer to https://sagemaker.readthedocs.io/en/stable/api/inference/transformer.html#sagemaker.transformer.Transformer for all options.
+                    Please refer to https://sagemaker.readthedocs.io/en/v2/api/inference/transformer.html#sagemaker.transformer.Transformer for all options.
                 6. transform_kwargs:
                     Any extra arguments needed to pass to transform.
                     Please refer to
-                    https://sagemaker.readthedocs.io/en/stable/api/inference/transformer.html#sagemaker.transformer.Transformer.transform for all options.
+                    https://sagemaker.readthedocs.io/en/v2/api/inference/transformer.html#sagemaker.transformer.Transformer.transform for all options.
         """
         if backend_kwargs is None:
             backend_kwargs = {}
@@ -374,9 +374,23 @@ def fit_predict(
             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, job_name, instance_type, instance_count, volume_size, custom_image_uri, wait,
-        backend_kwargs:
-            Same semantics as ``fit()``.
+        framework_version: str, default = `latest`
+            Training container version of autogluon. If `latest`, will use the latest available container 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.
+        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.
+        backend_kwargs: Optional[dict], default = None
+            Backend-specific arguments. Same keys as ``fit()``.
         predictions_path: Optional[str]
             S3 URL where predictions will be written by the training container (e.g.
             ``s3://my-bucket/runs/2024-05-01/predictions.csv``). The container's SageMaker execution role must