[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9669":3},{"id":4,"name":5,"fullName":6,"owner":5,"repo":5,"description":7,"homepage":8,"htmlUrl":9,"language":10,"languages":9,"totalLinesOfCode":9,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":15,"subscribersCount":15,"size":15,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":15,"forks30d":15,"starsTrendScore":19,"compositeScore":20,"rankGlobal":9,"rankLanguage":9,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":22,"hasPages":22,"topics":24,"createdAt":9,"pushedAt":9,"updatedAt":44,"readmeContent":45,"aiSummary":46,"trendingCount":15,"starSnapshotCount":15,"syncStatus":16,"lastSyncTime":47,"discoverSource":48},9669,"autogluon","autogluon\u002Fautogluon","Fast and Accurate ML in 3 Lines of Code","https:\u002F\u002Fauto.gluon.ai\u002F",null,"Python",10467,1163,100,374,0,2,20,158,11,93.2,"Apache License 2.0",false,"master",[5,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"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","2026-06-12 04:00:46","\n\n\u003Cdiv align=\"center\">\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F16392542\u002F77208906-224aa500-6aba-11ea-96bd-e81806074030.png\" width=\"350\">\n\n## Fast and Accurate ML in 3 Lines of Code\n\n[![Latest Release](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fv\u002Frelease\u002Fautogluon\u002Fautogluon)](https:\u002F\u002Fgithub.com\u002Fautogluon\u002Fautogluon\u002Freleases)\n[![Conda Forge](https:\u002F\u002Fimg.shields.io\u002Fconda\u002Fvn\u002Fconda-forge\u002Fautogluon.svg)](https:\u002F\u002Fanaconda.org\u002Fconda-forge\u002Fautogluon)\n[![Python Versions](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fpython-3.10%20%7C%203.11%20%7C%203.12%20%7C%203.13-blue)](https:\u002F\u002Fpypi.org\u002Fproject\u002Fautogluon\u002F)\n[![Downloads](https:\u002F\u002Fpepy.tech\u002Fbadge\u002Fautogluon\u002Fmonth)](https:\u002F\u002Fpepy.tech\u002Fproject\u002Fautogluon)\n[![GitHub license](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLicense-Apache_2.0-blue.svg)](.\u002FLICENSE)\n[![Discord](https:\u002F\u002Fimg.shields.io\u002Fdiscord\u002F1043248669505368144?color=7289da&label=Discord&logo=discord&logoColor=ffffff)](https:\u002F\u002Fdiscord.gg\u002FwjUmjqAc2N)\n[![Twitter](https:\u002F\u002Fimg.shields.io\u002Ftwitter\u002Ffollow\u002Fautogluon?style=social)](https:\u002F\u002Ftwitter.com\u002Fautogluon)\n[![Continuous Integration](https:\u002F\u002Fgithub.com\u002Fautogluon\u002Fautogluon\u002Factions\u002Fworkflows\u002Fcontinuous_integration.yml\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002Fautogluon\u002Fautogluon\u002Factions\u002Fworkflows\u002Fcontinuous_integration.yml)\n[![Platform Tests](https:\u002F\u002Fgithub.com\u002Fautogluon\u002Fautogluon\u002Factions\u002Fworkflows\u002Fplatform_tests-command.yml\u002Fbadge.svg?event=schedule)](https:\u002F\u002Fgithub.com\u002Fautogluon\u002Fautogluon\u002Factions\u002Fworkflows\u002Fplatform_tests-command.yml)\n\n[Installation](https:\u002F\u002Fauto.gluon.ai\u002Fstable\u002Finstall.html) | [Documentation](https:\u002F\u002Fauto.gluon.ai\u002Fstable\u002Findex.html) | [Release Notes](https:\u002F\u002Fauto.gluon.ai\u002Fstable\u002Fwhats_new\u002Findex.html)\n\n\u003C\u002Fdiv>\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:\u002F\u002Fauto.gluon.ai\u002Fstable\u002Finstall.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:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=&message=tutorial&color=grey)](https:\u002F\u002Fauto.gluon.ai\u002Fstable\u002Ftutorials\u002Ftabular\u002Ftabular-quick-start.html) |                 [![API](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fapi-reference-blue.svg)](https:\u002F\u002Fauto.gluon.ai\u002Fstable\u002Fapi\u002Fautogluon.tabular.TabularPredictor.html)                 |\n| TimeSeriesPredictor | [![Quick Start](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=&message=tutorial&color=grey)](https:\u002F\u002Fauto.gluon.ai\u002Fstable\u002Ftutorials\u002Ftimeseries\u002Fforecasting-quick-start.html)            | [![API](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fapi-reference-blue.svg)](https:\u002F\u002Fauto.gluon.ai\u002Fstable\u002Fapi\u002Fautogluon.timeseries.TimeSeriesPredictor.html) |\n| MultiModalPredictor | [![Quick Start](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=&message=tutorial&color=grey)](https:\u002F\u002Fauto.gluon.ai\u002Fstable\u002Ftutorials\u002Fmultimodal\u002Fmultimodal_prediction\u002Fmultimodal-quick-start.html)            | [![API](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fapi-reference-blue.svg)](https:\u002F\u002Fauto.gluon.ai\u002Fstable\u002Fapi\u002Fautogluon.multimodal.MultiModalPredictor.html) |\n\n## :mag: Resources\n\n### Hands-on Tutorials \u002F 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: [Structured Foundation Models Meets AutoML](https:\u002F\u002Ficml.cc\u002Fvirtual\u002F2025\u002F46786)                                                                               | Expo Talk       | [ICML 2025](https:\u002F\u002Ficml.cc\u002FConferences\u002F2025)                                                                                      | 2025\u002F07\u002F13  |\n| :tv: [AutoGluon 1.2: Advancing AutoML with Foundational Models and LLM Agents](https:\u002F\u002Fneurips.cc\u002Fvirtual\u002F2024\u002Fexpo-workshop\u002F100328)                               | Expo Workshop   | [NeurIPS 2024](https:\u002F\u002Fneurips.cc\u002FConferences\u002F2024)                                                                                | 2024\u002F12\u002F10  |\n| :tv: [AutoGluon: Towards No-Code Automated Machine Learning](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=SwPq9qjaN2Q)                | Tutorial | [AutoML 2024](https:\u002F\u002F2024.automl.cc\u002F)                                           | 2024\u002F09\u002F09 |\n| :tv: [AutoGluon 1.0: Shattering the AutoML Ceiling with Zero Lines of Code](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=5tvp_Ihgnuk) | Tutorial | [AutoML 2023](https:\u002F\u002F2023.automl.cc\u002F)                                           | 2023\u002F09\u002F12 |\n| :sound: [AutoGluon: The Story](https:\u002F\u002Fautomlpodcast.com\u002Fepisode\u002Fautogluon-the-story)                                    | Podcast  | [The AutoML Podcast](https:\u002F\u002Fautomlpodcast.com\u002F)                                 | 2023\u002F09\u002F05 |\n| :tv: [AutoGluon: AutoML for Tabular, Multimodal, and Time Series Data](https:\u002F\u002Fyoutu.be\u002FLwu15m5mmbs?si=jSaFJDqkTU27C0fa) | Tutorial | PyData Berlin                                                                    | 2023\u002F06\u002F20 |\n| :tv: [Solving Complex ML Problems in a few Lines of Code with AutoGluon](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=J1UQUCPB88I)    | Tutorial | PyData Seattle                                                                   | 2023\u002F06\u002F20 |\n| :tv: [The AutoML Revolution](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=VAAITEds-28)                                                | Tutorial | [Fall AutoML School 2022](https:\u002F\u002Fsites.google.com\u002Fview\u002Fautoml-fall-school-2022) | 2022\u002F10\u002F18 |\n\n### Scientific Publications\n- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2003.06505.pdf) (*Arxiv*, 2020) ([BibTeX](CITING.md#general-usage--autogluontabular))\n- [Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F62d75fb2e3075506e8837d8f55021ab1-Abstract.html) (*NeurIPS*, 2020) ([BibTeX](CITING.md#tabular-distillation))\n- [Benchmarking Multimodal AutoML for Tabular Data with Text Fields](https:\u002F\u002Fdatasets-benchmarks-proceedings.neurips.cc\u002Fpaper\u002F2021\u002Ffile\u002F9bf31c7ff062936a96d3c8bd1f8f2ff3-Paper-round2.pdf) (*NeurIPS*, 2021) ([BibTeX](CITING.md#autogluonmultimodal))\n- [XTab: Cross-table Pretraining for Tabular Transformers](https:\u002F\u002Fproceedings.mlr.press\u002Fv202\u002Fzhu23k\u002Fzhu23k.pdf) (*ICML*, 2023)\n- [AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.05566) (*AutoML Conf*, 2023) ([BibTeX](CITING.md#autogluontimeseries))\n- [TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2311.02971.pdf) (*AutoML Conf*, 2024)\n- [AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2404.16233) (*AutoML Conf*, 2024) ([BibTeX](CITING.md#autogluonmultimodal))\n- [Chronos: Learning the Language of Time Series](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.07815) (*TMLR*, 2024)\n- [Multi-layer Stack Ensembles for Time Series Forecasting](https:\u002F\u002Farxiv.org\u002Fabs\u002F2511.15350) (*AutoML Conf*, 2025) ([BibTeX](CITING.md#autogluontimeseries))\n- [Chronos-2: From Univariate to Universal Forecasting](https:\u002F\u002Farxiv.org\u002Fabs\u002F2510.15821) (*Arxiv*, 2025) ([BibTeX](CITING.md#autogluontimeseries))\n- [TabArena: A Living Benchmark for Machine Learning on Tabular Data](https:\u002F\u002Farxiv.org\u002Fabs\u002F2506.16791) (*NeurIPS Spotlight*, 2025)\n- [Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F2510.21204) (*NeurIPS*, 2025)\n- [MLZero: A Multi-Agent System for End-to-end Machine Learning Automation](https:\u002F\u002Farxiv.org\u002Fabs\u002F2505.13941) (*NeurIPS*, 2025)\n- [fev-bench: A Realistic Benchmark for Time Series Forecasting](https:\u002F\u002Farxiv.org\u002Fabs\u002F2509.26468) (*Arxiv*, 2025)\n\n### Articles\n- [AutoGluon-TimeSeries: Every Time Series Forecasting Model In One Library](https:\u002F\u002Ftowardsdatascience.com\u002Fautogluon-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:\u002F\u002Faws.amazon.com\u002Fblogs\u002Fopensource\u002Fmachine-learning-with-autogluon-an-open-source-automl-library\u002F) (*AWS Open Source Blog*, Mar 2020)\n- [AutoGluon overview & example applications](https:\u002F\u002Ftowardsdatascience.com\u002Fautogluon-deep-learning-automl-5cdb4e2388ec?source=friends_link&sk=e3d17d06880ac714e47f07f39178fdf2) (*Towards Data Science*, Dec 2019)\n\n### Train\u002FDeploy AutoGluon in the Cloud\n- [AutoGluon Cloud](https:\u002F\u002Fauto.gluon.ai\u002Fcloud\u002Fstable\u002Findex.html) (Recommended)\n- [AutoGluon on Amazon SageMaker](https:\u002F\u002Fauto.gluon.ai\u002Fstable\u002Ftutorials\u002Fcloud_fit_deploy\u002Fcloud-aws-sagemaker-train-deploy.html)\n- [AutoGluon Deep Learning Containers](https:\u002F\u002Fgithub.com\u002Faws\u002Fdeep-learning-containers\u002Fblob\u002Fmaster\u002Favailable_images.md#autogluon-training-containers) (Security certified & maintained by the AutoGluon developers)\n- [AutoGluon Official Docker Container](https:\u002F\u002Fhub.docker.com\u002Fr\u002Fautogluon\u002Fautogluon)\n\n#### Outdated \u002F Unsupported Cloud Options\n- [AutoGluon on SageMaker AutoPilot](https:\u002F\u002Fauto.gluon.ai\u002Fstable\u002Ftutorials\u002Fcloud_fit_deploy\u002Fautopilot-autogluon.html) (Uses an old AutoGluon 0.4 release)\n- [AutoGluon-Tabular on AWS Marketplace](https:\u002F\u002Faws.amazon.com\u002Fmarketplace\u002Fpp\u002Fprodview-n4zf5pmjt7ism) (Outdated and 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:\u002F\u002Fgithub.com\u002Fautogluon\u002Fautogluon\u002Fblob\u002Fmaster\u002FCONTRIBUTING.md) to get started.\n\n## :classical_building: License\n\nThis library is licensed under the Apache 2.0 License.\n","AutoGluon 是一个由 AWS AI 开发的自动化机器学习工具，旨在通过几行代码实现快速且准确的模型训练与部署。其核心功能包括自动化的超参数优化、集成学习以及支持多种数据类型（如图像、文本、时间序列和表格数据）的处理。技术上，AutoGluon 基于 Python 构建，并集成了 PyTorch 和 scikit-learn 等流行的机器学习库。它特别适合那些希望简化机器学习流程但又不牺牲性能的应用场景，无论是初学者还是经验丰富的数据科学家都能从中受益，特别是在需要快速原型开发或对模型精度有较高要求的情况下。","2026-06-11 03:24:07","top_topic"]