{"id":13487364,"url":"https://github.com/autogluon/autogluon","last_synced_at":"2026-01-15T22:18:57.763Z","repository":{"id":37248292,"uuid":"199509705","full_name":"autogluon/autogluon","owner":"autogluon","description":"Fast and Accurate ML in 3 Lines of Code","archived":false,"fork":false,"pushed_at":"2025-05-01T23:06:09.000Z","size":22955,"stargazers_count":8793,"open_issues_count":403,"forks_count":1010,"subscribers_count":95,"default_branch":"master","last_synced_at":"2025-05-05T14:27:33.294Z","etag":null,"topics":["autogluon","automated-machine-learning","automl","computer-vision","data-science","deep-learning","ensemble-learning","forecasting","gluon","hyperparameter-optimization","machine-learning","natural-language-processing","object-detection","python","pytorch","scikit-learn","structured-data","tabular-data","time-series","transfer-learning"],"latest_commit_sha":null,"homepage":"https://auto.gluon.ai/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/autogluon.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":"SECURITY.md","support":null,"governance":null,"roadmap":"ROADMAP.md","authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2019-07-29T18:51:24.000Z","updated_at":"2025-05-05T10:21:30.000Z","dependencies_parsed_at":"2022-07-10T15:00:27.088Z","dependency_job_id":"a8a041d2-c66f-4975-b84f-503db13328e3","html_url":"https://github.com/autogluon/autogluon","commit_stats":{"total_commits":2336,"total_committers":137,"mean_commits":17.05109489051095,"dds":0.7174657534246576,"last_synced_commit":"4c428f68fc673e1562dd4261ada67ccc81134ffd"},"previous_names":["awslabs/autogluon"],"tags_count":35,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/autogluon%2Fautogluon","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/autogluon%2Fautogluon/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/autogluon%2Fautogluon/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/autogluon%2Fautogluon/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/autogluon","download_url":"https://codeload.github.com/autogluon/autogluon/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253547173,"owners_count":21925540,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["autogluon","automated-machine-learning","automl","computer-vision","data-science","deep-learning","ensemble-learning","forecasting","gluon","hyperparameter-optimization","machine-learning","natural-language-processing","object-detection","python","pytorch","scikit-learn","structured-data","tabular-data","time-series","transfer-learning"],"created_at":"2024-07-31T18:00:58.234Z","updated_at":"2026-01-15T22:18:57.757Z","avatar_url":"https://github.com/autogluon.png","language":"Python","readme":"\n\n\u003cdiv align=\"center\"\u003e\n\u003cimg src=\"https://user-images.githubusercontent.com/16392542/77208906-224aa500-6aba-11ea-96bd-e81806074030.png\" width=\"350\"\u003e\n\n## Fast and Accurate ML in 3 Lines of Code\n\n[![Latest Release](https://img.shields.io/github/v/release/autogluon/autogluon)](https://github.com/autogluon/autogluon/releases)\n[![Conda Forge](https://img.shields.io/conda/vn/conda-forge/autogluon.svg)](https://anaconda.org/conda-forge/autogluon)\n[![Python Versions](https://img.shields.io/badge/python-3.10%20%7C%203.11%20%7C%203.12%20%7C%203.13-blue)](https://pypi.org/project/autogluon/)\n[![Downloads](https://pepy.tech/badge/autogluon/month)](https://pepy.tech/project/autogluon)\n[![GitHub license](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](./LICENSE)\n[![Discord](https://img.shields.io/discord/1043248669505368144?color=7289da\u0026label=Discord\u0026logo=discord\u0026logoColor=ffffff)](https://discord.gg/wjUmjqAc2N)\n[![Twitter](https://img.shields.io/twitter/follow/autogluon?style=social)](https://twitter.com/autogluon)\n[![Continuous Integration](https://github.com/autogluon/autogluon/actions/workflows/continuous_integration.yml/badge.svg)](https://github.com/autogluon/autogluon/actions/workflows/continuous_integration.yml)\n[![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)\n\n[Installation](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\u003c/div\u003e\n\nAutoGluon, developed by AWS AI, 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\n## 💾 Installation\n\nAutoGluon is supported on Python 3.10 - 3.13 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## :zap: 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\", presets=\"best\")\npredictions = predictor.predict(\"test.csv\")\n```\n\n| AutoGluon Task      |                                                                                Quickstart                                                                                |                                                                                API                                                                                |\n|:--------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------:|\n| TabularPredictor    | [![Quick Start](https://img.shields.io/static/v1?label=\u0026message=tutorial\u0026color=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)                 |\n| TimeSeriesPredictor | [![Quick Start](https://img.shields.io/static/v1?label=\u0026message=tutorial\u0026color=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) |\n| MultiModalPredictor | [![Quick Start](https://img.shields.io/static/v1?label=\u0026message=tutorial\u0026color=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) |\n\n## :mag: 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| :tv: [AutoGluon: Towards No-Code Automated Machine Learning](https://www.youtube.com/watch?v=SwPq9qjaN2Q)                | Tutorial | [AutoML 2024](https://2024.automl.cc/)                                           | 2024/09/09 |\n| :tv: [AutoGluon 1.0: Shattering the AutoML Ceiling with Zero Lines of Code](https://www.youtube.com/watch?v=5tvp_Ihgnuk) | Tutorial | [AutoML 2023](https://2023.automl.cc/)                                           | 2023/09/12 |\n| :sound: [AutoGluon: The Story](https://automlpodcast.com/episode/autogluon-the-story)                                    | Podcast  | [The AutoML Podcast](https://automlpodcast.com/)                                 | 2023/09/05 |\n| :tv: [AutoGluon: AutoML for Tabular, Multimodal, and Time Series Data](https://youtu.be/Lwu15m5mmbs?si=jSaFJDqkTU27C0fa) | Tutorial | PyData Berlin                                                                    | 2023/06/20 |\n| :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 |\n| :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 |\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) (*AutoML Conf*, 2024)\n- [AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models](https://arxiv.org/pdf/2404.16233) (*AutoML Conf*, 2024) ([BibTeX](CITING.md#autogluonmultimodal))\n- [Multi-layer Stack Ensembles for Time Series Forecasting](https://arxiv.org/abs/2511.15350) (*AutoML Conf*, 2025) ([BibTeX](CITING.md#autogluontimeseries))\n- [Chronos-2: From Univariate to Universal Forecasting](https://arxiv.org/abs/2510.15821) (*Arxiv*, 2025) ([BibTeX](CITING.md#autogluontimeseries))\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 \u0026 example applications](https://towardsdatascience.com/autogluon-deep-learning-automl-5cdb4e2388ec?source=friends_link\u0026sk=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 \u0026 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## :pencil: Citing AutoGluon\n\nIf you use AutoGluon in a scientific publication, please refer to our [citation guide](CITING.md).\n\n## :wave: 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## :classical_building: License\n\nThis library is licensed under the Apache 2.0 License.\n","funding_links":[],"categories":["Python","The Data Science Toolbox","🎯 Tool Categories","AutoML","time-series","Frameworks \u0026 Platforms","Implementations","General tools","\u003ca name=\"Python\"\u003e\u003c/a\u003ePython"],"sub_categories":["Miscellaneous Tools","🛠️ AutoML \u0026 Model Training","Other Frameworks"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fautogluon%2Fautogluon","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fautogluon%2Fautogluon","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fautogluon%2Fautogluon/lists"}