Clean up tutorials and API documentation

.gitignore

@@ -70,6 +70,7 @@ instance/
 
 # Sphinx documentation
 docs/_build/
+docs/api/autogluon.cloud.*.rst
 
 # PyBuilder
 target/
@@ -133,4 +134,4 @@ VERSION.minor
 .idea
 .vscode
 .DS_Store
-results.xml
+results.xml

README.md

@@ -1,43 +1,91 @@
 
 
-<div align="left">
-  <img src="https://user-images.githubusercontent.com/16392542/77208906-224aa500-6aba-11ea-96bd-e81806074030.png" width="350">
-</div>
+<div align="center">
+<img src="https://user-images.githubusercontent.com/16392542/77208906-224aa500-6aba-11ea-96bd-e81806074030.png" width="350">
 
-# AutoGluon-Cloud
+## 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)
+[![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)
 
 [AutoGluon-Cloud Documentation](https://auto.gluon.ai/cloud/stable/index.html) | [AutoGluon Documentation](https://auto.gluon.ai)
 
-AutoGluon-Cloud aims to provide user tools to train, fine-tune and deploy [AutoGluon](https://auto.gluon.ai/stable/index.html) backed models on the cloud. With just a few lines of codes, users could train a model and perform inference on the cloud without worrying about MLOps details such as resource management.
+</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.
+
+It supports two workflows:
 
-Currently, AutoGluon-Cloud supports [Amazon SageMaker](https://aws.amazon.com/sagemaker/) as the cloud backend.
+- **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.
+
+## 💾 Installation & setup
 
-## Installation
 ```bash
-pip install -U pip
-pip install -U setuptools wheel
 pip install autogluon.cloud
 ```
 
-## Example
+Then provision the IAM role and S3 bucket AutoGluon-Cloud needs on AWS:
+
 ```python
+from autogluon.cloud import bootstrap
 
+bootstrap()
+```
+
+See the [Setup tutorial](https://auto.gluon.ai/cloud/stable/tutorials/setup.html) for the full walkthrough, including how to register an existing role and bucket instead.
+
+## ⚙️ Train your own model
+
+```python
 from autogluon.cloud import TabularCloudPredictor
-import pandas as pd
-train_data = pd.read_csv("https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv")
-test_data = pd.read_csv("https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv")
-test_data.drop(columns=['class'], inplace=True)
-cloud_predictor = TabularCloudPredictor(cloud_output_path='YOUR_S3_BUCKET_PATH')
+
+# `train_data` and `test_data` can be a local path, S3 URL, or pandas DataFrame
+train_data = "https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv"
+test_data = "https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv"
+
+# Train
+cloud_predictor = TabularCloudPredictor()
 cloud_predictor.fit(
-    train_data=train_data,  # path or DataFrame
+    train_data=train_data,
     predictor_init_args={"label": "class"},  # passed to TabularPredictor()
     predictor_fit_args={"time_limit": 120},  # passed to TabularPredictor.fit()
 )
+
+# Real-time inference endpoint
 cloud_predictor.deploy()
 result = cloud_predictor.predict_real_time(test_data)
 cloud_predictor.cleanup_deployment()
-# Batch inference
+
+# Batch prediction
 result = cloud_predictor.predict(test_data)
 ```
+
+## 🚀 Run a pretrained foundation model
+
+```python
+from autogluon.cloud import TimeSeriesFoundationModel
+
+# `data` can be a local path, S3 URL, or pandas DataFrame
+data = "https://autogluon.s3.amazonaws.com/datasets/timeseries/m4_hourly_tiny/train.csv"
+
+model = TimeSeriesFoundationModel("chronos-2")
+
+# Batch prediction — no training required
+predictions = model.predict(
+    data=data,
+    target="target",
+    prediction_length=24,
+)
+
+# Real-time inference endpoint
+endpoint = model.deploy()
+predictions = endpoint.predict(
+    data=data,
+    target="target",
+    prediction_length=24,
+)
+endpoint.delete_endpoint()
+```

docs/_templates/autosummary/base.rst

@@ -1,4 +1,4 @@
-{{ objname | escape | underline}}
+{{ objname.split('.')[-1] | escape | underline}}
 
 .. currentmodule:: {{ module }}
 

docs/_templates/custom_class.rst

@@ -1,12 +1,10 @@
-{{ fullname | escape | underline}}
+{{ objname | escape | underline}}
 
 .. currentmodule:: {{ module }}
 
 .. autoclass:: {{ objname }}
 
    {% block methods %}
-   .. automethod:: __init__
-
    {% if methods %}
    .. rubric:: {{ _('Methods') }}
 

docs/api.rst

@@ -16,18 +16,25 @@ API
     :template: custom_class.rst
     :methods:
 
-    MultiModalCloudPredictor
+    TimeSeriesCloudPredictor
 
 .. autosummary::
     :toctree: api
     :template: custom_class.rst
     :methods:
 
-    TimeSeriesCloudPredictor
+    TimeSeriesFoundationModel
 
 .. autosummary::
     :toctree: api
-    :template: foundation_model_class.rst
+    :template: custom_class.rst
+    :methods:
 
-    TimeSeriesFoundationModel
+    TimeSeriesEndpoint
 
+.. autosummary::
+    :toctree: api
+    :template: custom_class.rst
+    :methods:
+
+    MultiModalCloudPredictor

docs/api/multimodal.rst

@@ -0,0 +1,10 @@
+Multimodal
+==========
+
+.. currentmodule:: autogluon.cloud
+
+.. autosummary::
+    :toctree: .
+    :template: custom_class.rst
+
+    MultiModalCloudPredictor

docs/api/setup.rst

@@ -0,0 +1,15 @@
+Setup
+=====
+
+Functions for managing AutoGluon-Cloud's AWS configuration. See the :doc:`Setup tutorial </tutorials/setup>` for usage.
+
+.. currentmodule:: autogluon.cloud
+
+.. autosummary::
+    :toctree: .
+    :nosignatures:
+
+    bootstrap
+    register
+    status
+    teardown

docs/api/tabular.rst

@@ -0,0 +1,10 @@
+Tabular
+=======
+
+.. currentmodule:: autogluon.cloud
+
+.. autosummary::
+    :toctree: .
+    :template: custom_class.rst
+
+    TabularCloudPredictor

docs/api/timeseries.rst

@@ -0,0 +1,12 @@
+Time Series
+===========
+
+.. currentmodule:: autogluon.cloud
+
+.. autosummary::
+    :toctree: .
+    :template: custom_class.rst
+
+    TimeSeriesCloudPredictor
+    TimeSeriesFoundationModel
+    TimeSeriesEndpoint

docs/conf.py

@@ -33,6 +33,9 @@
 
 autosummary_generate = True
 numpydoc_show_class_members = False
+# Merge __init__ docstring (with Parameters) into the class docstring,
+# and suppress the long signature lines on class/__init__ headers.
+autoclass_content = "both"
 
 googleanalytics_id = "UA-96378503-20"
 
@@ -43,7 +46,7 @@
 
 nb_execution_excludepatterns = ["jupyter_execute"]
 
-nb_dirs_to_exec = [os.path.join("tutorials", tag) for tag in tags if os.path.isdir(os.path.join("tutorials", tag))]
+nb_dirs_to_exec = [os.path.join("tutorials", tag) for tag in tags if os.path.isdir(os.path.join("tutorials", tag))]  # noqa: F821
 
 if len(nb_dirs_to_exec) > 0:
     nb_dirs_to_exclude = [
@@ -90,3 +93,14 @@
 html_static_path = ["_static"]
 html_css_files = ["custom.css"]
 html_js_files = ["custom.js"]
+
+
+def _skip_meta_private(app, what, name, obj, skip, options):
+    doc = getattr(obj, "__doc__", None) or ""
+    if ":meta private:" in doc:
+        return True
+    return None
+
+
+def setup(app):
+    app.connect("autodoc-skip-member", _skip_meta_private)