Package: autogluon.mxnet
AutoML for Text, Image, and Tabular DataAutoGluon 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# First install package from terminal:# python3 -m pip install -U pip# python3 -m pip install -U setuptools wheel# python3 -m pip install -U "mxnet<2.0.0"# python3 -m pip install autogluon  # autogluon==0.2.0fromautogluon.tabularimportTabularDataset,TabularPredictortrain_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 120sleaderboard=predictor.leaderboard(test_data)AutoGluon TaskQuickstartAPITabularPredictorTextPredictorImagePredictorObjectDetectorNewsAnnouncement for previous users:The AutoGluon codebase has been modularized intonamespace packages, which means you now only need those dependencies relevant to your prediction task of interest! For example, you can now work with tabular data without having toinstalldependencies required for AutoGluon's computer vision tasks (and vice versa). Unfortunately this improvement required a minor API change (eg. instead offrom autogluon import TabularPrediction, you should now do:from autogluon.tabular import TabularPredictor), for all versions newer than v0.0.15. Documentation/tutorials under the old API may still be viewedfor version 0.0.15which is the last released version under the old API.ResourcesSee theAutoGluon Websitefordocumentationand instructions on:Installing AutoGluonLearning with tabular dataTips to maximize accuracy(ifbenchmarking, make sure to runfit()with argumentpresets='best_quality').Learning with text dataLearning with image dataMore advanced topics such asNeural Architecture SearchScientific PublicationsAutoGluon-Tabular: Robust and Accurate AutoML for Structured Data(Arxiv, 2020)ArticlesAutoGluon for tabular data: 3 lines of code to achieve top 1% in Kaggle competitions(AWS Open Source Blog, Mar 2020)Accurate image classification in 3 lines of code with AutoGluon(Medium, Feb 2020)AutoGluon overview & example applications(Towards Data Science, Dec 2019)Hands-on TutorialsFrom HPO to NAS: Automated Deep Learning (CVPR 2020)Practical Automated Machine Learning with Tabular, Text, and Image Data (KDD 2020)Train/Deploy AutoGluon in the CloudAutoGluon-Tabular on AWS MarketplaceRunning AutoGluon-Tabular on Amazon SageMakerRunning AutoGluon Image Classification on Amazon SageMakerCiting AutoGluonIf 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."arXiv preprint arXiv:2003.06505 (2020).BibTeX entry:@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}}AutoGluon for Hyperparameter and Neural Architecture Search (HNAS)AutoGluon also provides state-of-the-art tools for neural hyperparameter and architecture search, such as for example ASHA, Hyperband, Bayesian Optimization and BOHB. To get started, checkout the following resourcesGeneral introduction into HNASIntroduction into HNAS with AutoGluonExample notebookExample scripts for efficient multi-fidelity HNAS of PyTorch neural network modelsAlso have a look at our paper"Model-based Asynchronous Hyperparameter and Neural Architecture Search"arXiv preprint arXiv:2003.10865 (2020).@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}}AutoGluon for Constrained Hyperparameter OptimizationAutoGluon includes analgorithm for constrained hyperparameter optimization. Check out our paper applying it to optimize model performance under fairness constraints:"Fair Bayesian Optimization", AIES (2021).@article{fairbo,title={Fair Bayesian Optimization},author={Perrone, Valerio and Donini, Michele and Zafar, Bilal Muhammad and Schmucker, Robin and Kenthapadi, Krishnaram and Archambeau, Cédric},journal={AIES},year={2021}}LicenseThis library is licensed under the Apache 2.0 License.Contributing to AutoGluonWe are actively accepting code contributions to the AutoGluon project. If you are interested in contributing to AutoGluon, please read theContributing Guideto get started.