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- Edit this `README.md` markdown file to author your organization card.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ <div align="center">
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+ <img src="https://user-images.githubusercontent.com/16392542/77208906-224aa500-6aba-11ea-96bd-e81806074030.png" width="350">
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+ ## AutoML for Image, Text, Time Series, and Tabular Data
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+ [![Latest Release](https://img.shields.io/github/v/release/autogluon/autogluon)](https://github.com/autogluon/autogluon/releases)
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+ [![Conda Forge](https://img.shields.io/conda/vn/conda-forge/autogluon.svg)](https://anaconda.org/conda-forge/autogluon)
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+ [![Python Versions](https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11-blue)](https://pypi.org/project/autogluon/)
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+ [![Downloads](https://pepy.tech/badge/autogluon/month)](https://pepy.tech/project/autogluon)
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+ [![GitHub license](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](./LICENSE)
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+ [![Discord](https://img.shields.io/discord/1043248669505368144?logo=discord&style=flat)](https://discord.gg/wjUmjqAc2N)
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+ [![Twitter](https://img.shields.io/twitter/follow/autogluon?style=social)](https://twitter.com/autogluon)
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+ [![Continuous Integration](https://github.com/autogluon/autogluon/actions/workflows/continuous_integration.yml/badge.svg)](https://github.com/autogluon/autogluon/actions/workflows/continuous_integration.yml)
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+ [![Platform Tests](https://github.com/autogluon/autogluon/actions/workflows/platform_tests-command.yml/badge.svg?event=schedule)](https://github.com/autogluon/autogluon/actions/workflows/platform_tests-command.yml)
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+ [Install Instructions](https://auto.gluon.ai/stable/install.html) | [Documentation](https://auto.gluon.ai/stable/index.html) | [Release Notes](https://auto.gluon.ai/stable/whats_new/index.html)
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+ AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models on image, text, time series, and tabular data.
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+ </div>
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+
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+ ## 💾 Installation
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+ AutoGluon is supported on Python 3.8 - 3.11 and is available on Linux, MacOS, and Windows.
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+ You can install AutoGluon with:
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+
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+ ```python
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+ pip install autogluon
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+ ```
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+
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+ Visit our [Installation Guide](https://auto.gluon.ai/stable/install.html) for detailed instructions, including GPU support, Conda installs, and optional dependencies.
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+
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+ ## :zap: Quickstart
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+
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+ Build accurate end-to-end ML models in just 3 lines of code!
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+
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+ ```python
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+ from autogluon.tabular import TabularPredictor
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+ predictor = TabularPredictor(label="class").fit("train.csv")
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+ predictions = predictor.predict("test.csv")
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+ ```
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+
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+ | AutoGluon Task | Quickstart | API |
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+ |:--------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------:|
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+ | TabularPredictor | [![Quick Start](https://img.shields.io/static/v1?label=&message=tutorial&color=grey)](https://auto.gluon.ai/stable/tutorials/tabular/tabular-quick-start.html) | [![API](https://img.shields.io/badge/api-reference-blue.svg)](https://auto.gluon.ai/stable/api/autogluon.tabular.TabularPredictor.html) |
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+ | MultiModalPredictor | [![Quick Start](https://img.shields.io/static/v1?label=&message=tutorial&color=grey)](https://auto.gluon.ai/stable/tutorials/multimodal/multimodal_prediction/multimodal-quick-start.html) | [![API](https://img.shields.io/badge/api-reference-blue.svg)](https://auto.gluon.ai/stable/api/autogluon.multimodal.MultiModalPredictor.html) |
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+ | TimeSeriesPredictor | [![Quick Start](https://img.shields.io/static/v1?label=&message=tutorial&color=grey)](https://auto.gluon.ai/stable/tutorials/timeseries/forecasting-quick-start.html) | [![API](https://img.shields.io/badge/api-reference-blue.svg)](https://auto.gluon.ai/stable/api/autogluon.timeseries.TimeSeriesPredictor.html) |
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+
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+ ## :mag: Resources
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+
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+ ### Hands-on Tutorials / Talks
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+
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+ Below is a curated list of recent tutorials and talks on AutoGluon. A comprehensive list is available [here](AWESOME.md#videos--tutorials).
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+ | Title | Format | Location | Date |
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+ |--------------------------------------------------------------------------------------------------------------------------|----------|----------------------------------------------------------------------------------|------------|
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+ | :tv: [AutoGluon 1.0: Shattering the AutoML Ceiling with Zero Lines of Code](https://www.youtube.com/watch?v=5tvp_Ihgnuk) | Tutorial | [AutoML Conf 2023](https://2023.automl.cc/) | 2023/09/12 |
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+ | :sound: [AutoGluon: The Story](https://automlpodcast.com/episode/autogluon-the-story) | Podcast | [The AutoML Podcast](https://automlpodcast.com/) | 2023/09/05 |
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+ | :tv: [AutoGluon: AutoML for Tabular, Multimodal, and Time Series Data](https://youtu.be/Lwu15m5mmbs?si=jSaFJDqkTU27C0fa) | Tutorial | PyData Berlin | 2023/06/20 |
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+ | :tv: [Solving Complex ML Problems in a few Lines of Code with AutoGluon](https://www.youtube.com/watch?v=J1UQUCPB88I) | Tutorial | PyData Seattle | 2023/06/20 |
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+ | :tv: [The AutoML Revolution](https://www.youtube.com/watch?v=VAAITEds-28) | Tutorial | [Fall AutoML School 2022](https://sites.google.com/view/automl-fall-school-2022) | 2022/10/18 |
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+
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+ ### Scientific Publications
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+ - [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://arxiv.org/pdf/2003.06505.pdf) (*Arxiv*, 2020) ([BibTeX](CITING.md#general-usage--autogluontabular))
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+ - [Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation](https://proceedings.neurips.cc/paper/2020/hash/62d75fb2e3075506e8837d8f55021ab1-Abstract.html) (*NeurIPS*, 2020) ([BibTeX](CITING.md#tabular-distillation))
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+ - [Benchmarking Multimodal AutoML for Tabular Data with Text Fields](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/9bf31c7ff062936a96d3c8bd1f8f2ff3-Paper-round2.pdf) (*NeurIPS*, 2021) ([BibTeX](CITING.md#autogluonmultimodal))
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+ - [XTab: Cross-table Pretraining for Tabular Transformers](https://proceedings.mlr.press/v202/zhu23k/zhu23k.pdf) (*ICML*, 2023)
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+ - [AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting](https://arxiv.org/abs/2308.05566) (*AutoML Conf*, 2023) ([BibTeX](CITING.md#autogluontimeseries))
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+ - [TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications](https://arxiv.org/pdf/2311.02971.pdf) (*Under Review*, 2024)
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+
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+ ### Articles
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+ - [AutoGluon-TimeSeries: Every Time Series Forecasting Model In One Library](https://towardsdatascience.com/autogluon-timeseries-every-time-series-forecasting-model-in-one-library-29a3bf6879db) (*Towards Data Science*, Jan 2024)
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+ - [AutoGluon for tabular data: 3 lines of code to achieve top 1% in Kaggle competitions](https://aws.amazon.com/blogs/opensource/machine-learning-with-autogluon-an-open-source-automl-library/) (*AWS Open Source Blog*, Mar 2020)
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+ - [AutoGluon overview & example applications](https://towardsdatascience.com/autogluon-deep-learning-automl-5cdb4e2388ec?source=friends_link&sk=e3d17d06880ac714e47f07f39178fdf2) (*Towards Data Science*, Dec 2019)
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+
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+ ### Train/Deploy AutoGluon in the Cloud
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+ - [AutoGluon Cloud](https://auto.gluon.ai/cloud/stable/index.html) (Recommended)
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+ - [AutoGluon on SageMaker AutoPilot](https://auto.gluon.ai/stable/tutorials/cloud_fit_deploy/autopilot-autogluon.html)
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+ - [AutoGluon on Amazon SageMaker](https://auto.gluon.ai/stable/tutorials/cloud_fit_deploy/cloud-aws-sagemaker-train-deploy.html)
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+ - [AutoGluon Deep Learning Containers](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#autogluon-training-containers) (Security certified & maintained by the AutoGluon developers)
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+ - [AutoGluon Official Docker Container](https://hub.docker.com/r/autogluon/autogluon)
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+ - [AutoGluon-Tabular on AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-n4zf5pmjt7ism) (Not maintained by us)
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+
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+ ## :pencil: Citing AutoGluon
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+ If you use AutoGluon in a scientific publication, please refer to our [citation guide](CITING.md).
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+ ## :wave: How to get involved
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+ We are actively accepting code contributions to the AutoGluon project. If you are interested in contributing to AutoGluon, please read the [Contributing Guide](https://github.com/autogluon/autogluon/blob/master/CONTRIBUTING.md) to get started.
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+ ## :classical_building: License
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+ This library is licensed under the Apache 2.0 License.