autogluon

community

https://auto.gluon.ai/stable/index.html

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AutoML for Image, Text, Time Series, and Tabular Data

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\n \n $\"Latest$ \n \n \n $\"Conda$ \n \n \n $\"Python$ \n \n \n $\"Downloads\"$ \n \n \n $\"GitHub$ \n \n \n $\"Discord\"$ \n \n \n $\"Twitter\"$ \n \n

\n \n\n[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)\n\n

\n\n\nAutoGluon 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.\n\n## 💾 Installation\n\nAutoGluon is supported on Python 3.8 - 3.11 and is available on Linux, MacOS, and Windows.\n\nYou can install AutoGluon with:\n\n```python\npip install autogluon\n```\n\nVisit our [Installation Guide](https://auto.gluon.ai/stable/install.html) for detailed instructions, including GPU support, Conda installs, and optional dependencies.\n\n## ⚡ Quickstart\n\nBuild accurate end-to-end ML models in just 3 lines of code!\n\n```python\nfrom autogluon.tabular import TabularPredictor\npredictor = TabularPredictor(label=\"class\").fit(\"train.csv\")\npredictions = predictor.predict(\"test.csv\")\n```\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n

AutoGluon Task	Quickstart	API
TabularPredictor	\n \n $\"Quick$ \n \n	\n \n $\"API\"$ \n \n
TimeSeriesPredictor	\n \n $\"Quick$ \n \n	\n \n $\"API\"$ \n \n
MultiModalPredictor	\n \n $\"Quick$ \n \n	\n \n $\"API\"$ \n \n

\n\n\n## 🔍 Resources\n\n### Hands-on Tutorials / Talks\n\nBelow is a curated list of recent tutorials and talks on AutoGluon. A comprehensive list is available [here](AWESOME.md#videos--tutorials).\n\n| Title | Format | Location | Date |\n|--------------------------------------------------------------------------------------------------------------------------|----------|----------------------------------------------------------------------------------|------------|\n| 📺 [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 |\n| 🔉 [AutoGluon: The Story](https://automlpodcast.com/episode/autogluon-the-story) | Podcast | [The AutoML Podcast](https://automlpodcast.com/) | 2023/09/05 |\n| 📺 [AutoGluon: AutoML for Tabular, Multimodal, and Time Series Data](https://youtu.be/Lwu15m5mmbs?si=jSaFJDqkTU27C0fa) | Tutorial | PyData Berlin | 2023/06/20 | \n| 📺 [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 | \n| 📺 [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 |\n\n### Scientific Publications\n- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://arxiv.org/pdf/2003.06505.pdf) (*Arxiv*, 2020) ([BibTeX](CITING.md#general-usage--autogluontabular))\n- [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))\n- [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))\n- [XTab: Cross-table Pretraining for Tabular Transformers](https://proceedings.mlr.press/v202/zhu23k/zhu23k.pdf) (*ICML*, 2023)\n- [AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting](https://arxiv.org/abs/2308.05566) (*AutoML Conf*, 2023) ([BibTeX](CITING.md#autogluontimeseries))\n- [TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications](https://arxiv.org/pdf/2311.02971.pdf) (*Under Review*, 2024)\n\n### Articles\n- [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)\n- [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)\n- [AutoGluon overview & example applications](https://towardsdatascience.com/autogluon-deep-learning-automl-5cdb4e2388ec?source=friends_link&sk=e3d17d06880ac714e47f07f39178fdf2) (*Towards Data Science*, Dec 2019)\n\n### Train/Deploy AutoGluon in the Cloud\n- [AutoGluon Cloud](https://auto.gluon.ai/cloud/stable/index.html) (Recommended)\n- [AutoGluon on SageMaker AutoPilot](https://auto.gluon.ai/stable/tutorials/cloud_fit_deploy/autopilot-autogluon.html)\n- [AutoGluon on Amazon SageMaker](https://auto.gluon.ai/stable/tutorials/cloud_fit_deploy/cloud-aws-sagemaker-train-deploy.html)\n- [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)\n- [AutoGluon Official Docker Container](https://hub.docker.com/r/autogluon/autogluon)\n- [AutoGluon-Tabular on AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-n4zf5pmjt7ism) (Not maintained by us)\n\n## 📝 Citing AutoGluon\n\nIf you use AutoGluon in a scientific publication, please refer to our [citation guide](CITING.md).\n\n## 👋 How to get involved\n\nWe 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.\n\n## 🏛️ License\n\nThis library is licensed under the Apache 2.0 License.\n","html":"

\n\n

AutoML for Image, Text, Time Series, and Tabular Data

\n\n\n

\n \n $\"Latest$ \n \n \n $\"Conda$ \n \n \n $\"Python$ \n \n \n $\"Downloads\"$ \n \n \n $\"GitHub$ \n \n \n $\"Discord\"$ \n \n \n $\"Twitter\"$ \n \n

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Install Instructions | Documentation | Release Notes

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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.

💾 Installation

AutoGluon is supported on Python 3.8 - 3.11 and is available on Linux, MacOS, and Windows.

You can install AutoGluon with:

pip install autogluon\n

Visit our Installation Guide for detailed instructions, including GPU support, Conda installs, and optional dependencies.

⚡ Quickstart

Build accurate end-to-end ML models in just 3 lines of code!

from autogluon.tabular import TabularPredictor\npredictor = TabularPredictor(label=\"class\").fit(\"train.csv\")\npredictions = predictor.predict(\"test.csv\")\n

\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n

AutoGluon Task	Quickstart	API
TabularPredictor	\n \n $\"Quick$ \n \n	\n \n $\"API\"$ \n \n
TimeSeriesPredictor	\n \n $\"Quick$ \n \n	\n \n $\"API\"$ \n \n
MultiModalPredictor	\n \n $\"Quick$ \n \n	\n \n $\"API\"$ \n \n

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🔍 Resources

Hands-on Tutorials / Talks

Below is a curated list of recent tutorials and talks on AutoGluon. A comprehensive list is available here.

\n\t\n\t\t\n\n\n\n\n\n\n\t\t\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\t

Title	Format	Location	Date
📺 AutoGluon 1.0: Shattering the AutoML Ceiling with Zero Lines of Code	Tutorial	AutoML Conf 2023	2023/09/12
🔉 AutoGluon: The Story	Podcast	The AutoML Podcast	2023/09/05
📺 AutoGluon: AutoML for Tabular, Multimodal, and Time Series Data	Tutorial	PyData Berlin	2023/06/20
📺 Solving Complex ML Problems in a few Lines of Code with AutoGluon	Tutorial	PyData Seattle	2023/06/20
📺 The AutoML Revolution	Tutorial	Fall AutoML School 2022	2022/10/18

📝 Citing AutoGluon

If you use AutoGluon in a scientific publication, please refer to our citation guide.

👋 How to get involved

We are actively accepting code contributions to the AutoGluon project. If you are interested in contributing to AutoGluon, please read the Contributing Guide to get started.

🏛️ License

This library is licensed under the Apache 2.0 License.

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AI & ML interests

AutoML, text, tabular, multimodal, timeseries

Recent Activity

shchuro updated a Space 8 days ago

autogluon/fev-leaderboard

shchuro updated a dataset 8 days ago

autogluon/chronos_datasets

shchuro updated a model 25 days ago

autogluon/chronos-bolt-base

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AutoML for Image, Text, Time Series, and Tabular Data

Install Instructions | Documentation | Release Notes

💾 Installation

AutoGluon is supported on Python 3.8 - 3.11 and is available on Linux, MacOS, and Windows.

You can install AutoGluon with:

pip install autogluon

Visit our Installation Guide for detailed instructions, including GPU support, Conda installs, and optional dependencies.

⚡ Quickstart

Build accurate end-to-end ML models in just 3 lines of code!

from autogluon.tabular import TabularPredictor
predictor = TabularPredictor(label="class").fit("train.csv")
predictions = predictor.predict("test.csv")

AutoGluon Task	Quickstart	API
TabularPredictor
TimeSeriesPredictor
MultiModalPredictor

🔍 Resources

Hands-on Tutorials / Talks

Below is a curated list of recent tutorials and talks on AutoGluon. A comprehensive list is available here.

Title	Format	Location	Date
📺 AutoGluon 1.0: Shattering the AutoML Ceiling with Zero Lines of Code	Tutorial	AutoML Conf 2023	2023/09/12
🔉 AutoGluon: The Story	Podcast	The AutoML Podcast	2023/09/05
📺 AutoGluon: AutoML for Tabular, Multimodal, and Time Series Data	Tutorial	PyData Berlin	2023/06/20
📺 Solving Complex ML Problems in a few Lines of Code with AutoGluon	Tutorial	PyData Seattle	2023/06/20
📺 The AutoML Revolution	Tutorial	Fall AutoML School 2022	2022/10/18

Scientific Publications

AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data (Arxiv, 2020) (BibTeX)
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation (NeurIPS, 2020) (BibTeX)
Benchmarking Multimodal AutoML for Tabular Data with Text Fields (NeurIPS, 2021) (BibTeX)
XTab: Cross-table Pretraining for Tabular Transformers (ICML, 2023)
AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting (AutoML Conf, 2023) (BibTeX)
TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications (Under Review, 2024)

Articles

AutoGluon-TimeSeries: Every Time Series Forecasting Model In One Library (Towards Data Science, Jan 2024)
AutoGluon for tabular data: 3 lines of code to achieve top 1% in Kaggle competitions (AWS Open Source Blog, Mar 2020)
AutoGluon overview & example applications (Towards Data Science, Dec 2019)

Train/Deploy AutoGluon in the Cloud

AutoGluon Cloud (Recommended)
AutoGluon on SageMaker AutoPilot
AutoGluon on Amazon SageMaker
AutoGluon Deep Learning Containers (Security certified & maintained by the AutoGluon developers)
AutoGluon Official Docker Container
AutoGluon-Tabular on AWS Marketplace (Not maintained by us)

📝 Citing AutoGluon

If you use AutoGluon in a scientific publication, please refer to our citation guide.

👋 How to get involved

We are actively accepting code contributions to the AutoGluon project. If you are interested in contributing to AutoGluon, please read the Contributing Guide to get started.