# autogluon **Repository Path**: Python_Ai_Road/autogluon ## Basic Information - **Project Name**: autogluon - **Description**: AutoGluon: AutoML for Text, Image, and Tabular Data - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2022-01-14 - **Last Updated**: 2022-01-14 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README
## AutoML for Text, Image, and Tabular Data [![Build Status](https://ci.gluon.ai/view/all/job/autogluon/job/master/badge/icon)](https://ci.gluon.ai/view/all/job/autogluon/job/master/) [![Pypi Version](https://img.shields.io/pypi/v/autogluon.svg)](https://pypi.org/project/autogluon/#history) [![GitHub license](docs/static/apache2.svg)](./LICENSE) [![Downloads](https://pepy.tech/badge/autogluon)](https://pepy.tech/project/autogluon) ![Upload Python Package](https://github.com/awslabs/autogluon/workflows/Upload%20Python%20Package/badge.svg) 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 text, image, and tabular data. ## Example ```python # First install package from terminal: # python3 -m pip install -U pip # python3 -m pip install -U setuptools wheel # python3 -m pip install autogluon # autogluon==0.3.1 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_prediction/tabular-quickstart.html) | [![API](https://img.shields.io/badge/api-reference-blue.svg)](https://auto.gluon.ai/stable/api/autogluon.predictor.html#module-0) | | TextPredictor | [![Quick Start](https://img.shields.io/static/v1?label=&message=tutorial&color=grey)](https://auto.gluon.ai/stable/tutorials/text_prediction/beginner.html) | [![API](https://img.shields.io/badge/api-reference-blue.svg)](https://auto.gluon.ai/stable/api/autogluon.predictor.html#module-3) | | ImagePredictor | [![Quick Start](https://img.shields.io/static/v1?label=&message=tutorial&color=grey)](https://auto.gluon.ai/stable/tutorials/image_prediction/beginner.html) | [![API](https://img.shields.io/badge/api-reference-blue.svg)](https://auto.gluon.ai/stable/api/autogluon.predictor.html#module-1) | | ObjectDetector | [![Quick Start](https://img.shields.io/static/v1?label=&message=tutorial&color=grey)](https://auto.gluon.ai/stable/tutorials/object_detection/beginner.html) | [![API](https://img.shields.io/badge/api-reference-blue.svg)](https://auto.gluon.ai/stable/api/autogluon.predictor.html#module-2) | ## Resources See the [AutoGluon Website](https://auto.gluon.ai/stable/index.html) for [documentation](https://auto.gluon.ai/stable/api/index.html) and instructions on: - [Installing AutoGluon](https://auto.gluon.ai/stable/index.html#installation) - [Learning with tabular data](https://auto.gluon.ai/stable/tutorials/tabular_prediction/tabular-quickstart.html) - [Tips to maximize accuracy](https://auto.gluon.ai/stable/tutorials/tabular_prediction/tabular-quickstart.html#maximizing-predictive-performance) (if **benchmarking**, make sure to run `fit()` with argument `presets='best_quality'`). - [Learning with text data](https://auto.gluon.ai/stable/tutorials/text_prediction/beginner.html) - [Learning with image data](https://auto.gluon.ai/stable/tutorials/image_prediction/beginner.html) - More advanced topics such as [Neural Architecture Search](https://auto.gluon.ai/stable/tutorials/nas/index.html) ### 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) - [Running AutoGluon-Tabular on Amazon SageMaker](https://github.com/awslabs/amazon-sagemaker-examples/blob/master/advanced_functionality/autogluon-tabular/AutoGluon_Tabular_SageMaker.ipynb) - [Running AutoGluon Image Classification on Amazon SageMaker](https://github.com/zhanghang1989/AutoGluon-Docker) ## 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} } ``` ## AutoGluon for Hyperparameter Optimization AutoGluon also provides state-of-the-art tools for hyperparameter optimization, such as for example ASHA, Hyperband, Bayesian Optimization and BOHB. To get started, 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. ## 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/awslabs/autogluon/blob/master/CONTRIBUTING.md) to get started.