AutoGluon: AutoML for Text, Image, Time Series, and Tabular Data
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`AutoGluon` enables easy-to-use and easy-to-extend AutoML with a focus on automated stack ensembling, deep learning, and real-world applications spanning image, text, and tabular data. Intended for both ML beginners and experts, `AutoGluon` enables you to:

- Quickly prototype deep learning and classical ML solutions for your raw data with a few lines of code.
- Automatically utilize state-of-the-art techniques (where appropriate) without expert knowledge.
- Leverage automatic hyperparameter tuning, model selection/ensembling, architecture search, and data processing.
- Easily improve/tune your bespoke models and data pipelines, or customize `AutoGluon` for your use-case.

.. note::

   Example using AutoGluon to train and deploy a high-performance model on a tabular dataset:

   >>> from autogluon.tabular import TabularDataset, TabularPredictor
   >>> train_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv')
   >>> test_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv')
   >>> predictor = TabularPredictor(label='class').fit(train_data=train_data)
   >>> predictions = predictor.predict(test_data)

   AutoGluon can be applied for prediction tasks that involve image and text data. For adopting state-of-the-art deep learning models for multimodal prediction problems, you may try `autogluon.multimodal`:

   >>> from autogluon.multimodal import MultiModalPredictor
   >>> from datasets import load_dataset
   >>> train_data = load_dataset("glue", 'mrpc')['train'].to_pandas().drop('idx', axis=1)
   >>> test_data = load_dataset("glue", 'mrpc')['validation'].to_pandas().drop('idx', axis=1)
   >>> predictor = MultiModalPredictor(label='label').fit(train_data)
   >>> predictions = predictor.predict(test_data)
   >>> score = predictor.evaluate(test_data)

   Example using AutoGluon to forecast future values of time series:

   >>> from autogluon.timeseries import TimeSeriesDataFrame, TimeSeriesPredictor
   >>> data = TimeSeriesDataFrame('https://autogluon.s3.amazonaws.com/datasets/timeseries/m4_hourly/train.csv')
   >>> predictor = TimeSeriesPredictor(target='target', prediction_length=48).fit(data)
   >>> predictions = predictor.predict(data)


Installation
------------

.. include:: install-include.rst


Quick Start
-----------

.. raw:: html
   :file: static/application.html


Tutorials
---------

.. container:: cards

   .. card::
      :title: Tabular Prediction
      :link: tutorials/tabular_prediction/index.html

      How to predict variables in tabular datasets.

   .. card::
      :title: Multimodal Prediction
      :link: tutorials/multimodal/index.html

      How to solve problems that contain Image, Text, and Tabular features at the same time.

   .. card::
      :title: Time Series Forecasting
      :link: tutorials/timeseries/index.html

      How to train time series models for forecasting.

   .. card::
      :title: Exploratory Data Analysis
      :link: tutorials/eda/index.html

      Tools to explore and visualize data.


Managed Solutions Featuring AutoGluon
-------------------------------------

.. raw:: html
   :file: static/managed_solutions.html

Launch Tutorial Notebooks
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.. raw:: html
   :file: static/platforms.html

.. toctree::
   :maxdepth: 3
   :hidden:

   tutorials/tabular_prediction/index
   tutorials/multimodal/index
   tutorials/timeseries/index
   tutorials/cloud_fit_deploy/index
   tutorials/eda/index
   cheatsheet.rst
   api/autogluon.predictor
   api/autogluon.features
   api/autogluon.tabular.models