Skip to main content

AutoML for Image, Text, and Tabular Data

Project description

AutoML for Image, Text, Time Series, and Tabular Data

Latest Release Continuous Integration Platform Tests Python Versions GitHub license Downloads Twitter

Install Instructions | Documentation (Stable | Latest)

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

# First install package from terminal:
# pip install -U pip
# pip install -U setuptools wheel
# pip install autogluon  # autogluon==0.7.0

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 API
MultiModalPredictor Quick Start API
TimeSeriesPredictor Quick Start API

Resources

See the AutoGluon Website for documentation and instructions on:

Refer to the AutoGluon Roadmap for details on upcoming features and releases.

Scientific Publications

Articles

Hands-on Tutorials

Train/Deploy AutoGluon in the Cloud

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 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." 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}
}

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." Advances in Neural Information Processing Systems 33 (2020).

BibTeX entry:

@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." 8th ICML Workshop on Automated Machine Learning (AutoML). 2021.

BibTeX entry:

@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'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.

To learn more, checkout 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}
}

License

This library is licensed under the Apache 2.0 License.

Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

autogluon.common-0.7.0b20230320.tar.gz (34.6 kB view details)

Uploaded Source

Built Distribution

autogluon.common-0.7.0b20230320-py3-none-any.whl (45.3 kB view details)

Uploaded Python 3

File details

Details for the file autogluon.common-0.7.0b20230320.tar.gz.

File metadata

File hashes

Hashes for autogluon.common-0.7.0b20230320.tar.gz
Algorithm Hash digest
SHA256 4917e4d5f68b959f6e0928fdc3b957855763626001d46247f84ba91051cd960e
MD5 2fc7de9c909f562e604801104041d0e6
BLAKE2b-256 f025a0d29016f5d90d033c501a671aa0c9b62dad3ebb3ef48fe1300b3272e30a

See more details on using hashes here.

File details

Details for the file autogluon.common-0.7.0b20230320-py3-none-any.whl.

File metadata

File hashes

Hashes for autogluon.common-0.7.0b20230320-py3-none-any.whl
Algorithm Hash digest
SHA256 eb27fd6a25215e44a35ca59dceacac828adb6119ebc76c7f7456bc26ed405154
MD5 93e2a33fa647663dc16ee4ee578c8faf
BLAKE2b-256 f817f9952a9d859f2ad886c0d8b1c65d15d1386332575a2a86d78f7352482dcb

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page