# autogluon **Repository Path**: easetime/autogluon ## Basic Information - **Project Name**: autogluon - **Description**: No description available - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2024-01-17 - **Last Updated**: 2024-01-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README
## AutoML for Image, Text, Time Series, and Tabular Data [![Latest Release](https://img.shields.io/github/v/release/autogluon/autogluon)](https://github.com/autogluon/autogluon/releases) [![Continuous Integration](https://github.com/autogluon/autogluon/actions/workflows/continuous_integration.yml/badge.svg)](https://github.com/autogluon/autogluon/actions/workflows/continuous_integration.yml) [![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) [![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/) [![GitHub license](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](./LICENSE) [![Downloads](https://pepy.tech/badge/autogluon/month)](https://pepy.tech/project/autogluon) [![](https://img.shields.io/discord/1043248669505368144?logo=discord&style=flat)](https://discord.gg/wjUmjqAc2N) [![Twitter](https://img.shields.io/twitter/follow/autogluon?style=social)](https://twitter.com/autogluon) [Install Instructions](https://auto.gluon.ai/stable/install.html) | Documentation ([Stable](https://auto.gluon.ai/stable/index.html) | [Latest](https://auto.gluon.ai/dev/index.html)) 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. ## Example ```python # First install package from terminal: # pip install -U pip # pip install -U setuptools wheel # pip install autogluon # autogluon==1.0.0 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, time_limit=120) # Fit models for 120s leaderboard = predictor.leaderboard(test_data) ``` | AutoGluon Task | Quickstart | API | |:--------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------:| | 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) | | 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) | | 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) | ## Resources See the [AutoGluon Website](https://auto.gluon.ai/stable/index.html) for documentation and instructions on: - [Installing AutoGluon](https://auto.gluon.ai/stable/index.html#installation) - [Learning with tabular data](https://auto.gluon.ai/stable/tutorials/tabular/tabular-quick-start.html) - [Tips to maximize accuracy](https://auto.gluon.ai/stable/tutorials/tabular/tabular-essentials.html#maximizing-predictive-performance) (if **benchmarking**, make sure to run `fit()` with argument `presets='best_quality'`). - [Learning with multimodal data (image, text, etc.)](https://auto.gluon.ai/stable/tutorials/multimodal/multimodal_prediction/multimodal-quick-start.html) - [Learning with time series data](https://auto.gluon.ai/stable/tutorials/timeseries/forecasting-quick-start.html) Refer to the [AutoGluon Roadmap](https://github.com/autogluon/autogluon/blob/master/ROADMAP.md) for details on upcoming features and releases. ### Scientific Publications - [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://arxiv.org/pdf/2003.06505.pdf) (*Arxiv*, 2020) - [Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation](https://proceedings.neurips.cc/paper/2020/hash/62d75fb2e3075506e8837d8f55021ab1-Abstract.html) (*NeurIPS*, 2020) - [Multimodal AutoML on Structured Tables with Text Fields](https://openreview.net/pdf?id=OHAIVOOl7Vl) (*ICML AutoML Workshop*, 2021) ### Articles - [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) - [Accurate image classification in 3 lines of code with AutoGluon](https://medium.com/@zhanghang0704/image-classification-on-kaggle-using-autogluon-fc896e74d7e8) (*Medium*, Feb 2020) - [AutoGluon overview & example applications](https://towardsdatascience.com/autogluon-deep-learning-automl-5cdb4e2388ec?source=friends_link&sk=e3d17d06880ac714e47f07f39178fdf2) (*Towards Data Science*, Dec 2019) ### Hands-on Tutorials - [Practical Automated Machine Learning with Tabular, Text, and Image Data (KDD 2020)](https://jwmueller.github.io/KDD20-tutorial/) ### Train/Deploy AutoGluon in the Cloud - [AutoGluon-Tabular on AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-n4zf5pmjt7ism) - [AutoGluon-Tabular on Amazon SageMaker](https://github.com/aws/amazon-sagemaker-examples/tree/master/advanced_functionality/autogluon-tabular-containers) - [AutoGluon Deep Learning Containers](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#autogluon-training-containers) ## Contributing to AutoGluon 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. ## Citing AutoGluon If you use AutoGluon in a scientific publication, please cite the following paper: Erickson, Nick, et al. ["AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data."](https://arxiv.org/abs/2003.06505) arXiv preprint arXiv:2003.06505 (2020). BibTeX entry: ```bibtex @article{agtabular, title={AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data}, author={Erickson, Nick and Mueller, Jonas and Shirkov, Alexander and Zhang, Hang and Larroy, Pedro and Li, Mu and Smola, Alexander}, journal={arXiv preprint arXiv:2003.06505}, year={2020} } ``` If you are using AutoGluon Tabular's model distillation functionality, please cite the following paper: Fakoor, Rasool, et al. ["Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation."](https://proceedings.neurips.cc/paper/2020/hash/62d75fb2e3075506e8837d8f55021ab1-Abstract.html) Advances in Neural Information Processing Systems 33 (2020). BibTeX entry: ```bibtex @article{agtabulardistill, title={Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation}, author={Fakoor, Rasool and Mueller, Jonas W and Erickson, Nick and Chaudhari, Pratik and Smola, Alexander J}, journal={Advances in Neural Information Processing Systems}, volume={33}, year={2020} } ``` If you use AutoGluon's multimodal text+tabular functionality in a scientific publication, please cite the following paper: Shi, Xingjian, et al. ["Multimodal AutoML on Structured Tables with Text Fields."](https://openreview.net/forum?id=OHAIVOOl7Vl) 8th ICML Workshop on Automated Machine Learning (AutoML). 2021. BibTeX entry: ```bibtex @inproceedings{agmultimodaltext, title={Multimodal AutoML on Structured Tables with Text Fields}, author={Shi, Xingjian and Mueller, Jonas and Erickson, Nick and Li, Mu and Smola, Alex}, booktitle={8th ICML Workshop on Automated Machine Learning (AutoML)}, year={2021} } ``` If you use AutoGluon's time series forecasting functionality in a scientific publication, please cite the following paper: ```bibtex @inproceedings{agtimeseries, title={{AutoGluon-TimeSeries}: {AutoML} for Probabilistic Time Series Forecasting}, author={Shchur, Oleksandr and Turkmen, Caner and Erickson, Nick and Shen, Huibin and Shirkov, Alexander and Hu, Tony and Wang, Yuyang}, booktitle={International Conference on Automated Machine Learning}, year={2023} } ``` ## AutoGluon for Hyperparameter Optimization AutoGluon's state-of-the-art tools for hyperparameter optimization, such as ASHA, Hyperband, Bayesian Optimization and BOHB have moved to the stand-alone package [syne-tune](https://github.com/awslabs/syne-tune). To learn more, checkout our paper ["Model-based Asynchronous Hyperparameter and Neural Architecture Search"](https://arxiv.org/abs/2003.10865) arXiv preprint arXiv:2003.10865 (2020). ```bibtex @article{abohb, title={Model-based Asynchronous Hyperparameter and Neural Architecture Search}, author={Klein, Aaron and Tiao, Louis and Lienart, Thibaut and Archambeau, Cedric and Seeger, Matthias}, journal={arXiv preprint arXiv:2003.10865}, year={2020} } ``` ## License This library is licensed under the Apache 2.0 License.