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 Broken Link Checker 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==0.8.2

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}
}

If you use AutoGluon's time series forecasting functionality in a scientific publication, please cite the following paper:

@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.

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.multimodal-0.8.3b20231005.tar.gz (286.8 kB view details)

Uploaded Source

Built Distribution

File details

Details for the file autogluon.multimodal-0.8.3b20231005.tar.gz.

File metadata

File hashes

Hashes for autogluon.multimodal-0.8.3b20231005.tar.gz
Algorithm Hash digest
SHA256 2e04906621643ddd3ce63e01760e7a1b77e78408a53f93273416f0b1ad13d0da
MD5 c14fd394fe9eb6321ee7d88289b4aeba
BLAKE2b-256 f5f5b37df0d59894f37f9efba3a358cc3b15e97532a355641e1872dea7d2d65b

See more details on using hashes here.

File details

Details for the file autogluon.multimodal-0.8.3b20231005-py3-none-any.whl.

File metadata

File hashes

Hashes for autogluon.multimodal-0.8.3b20231005-py3-none-any.whl
Algorithm Hash digest
SHA256 ab14a35e976ff274890217b2ac8a558a250b73a586e2bf8233551ed699207e80
MD5 ffc2e96522558dc93e04eb95fff9d242
BLAKE2b-256 5c0eb7c7cdf5c5db8231cd60591071927aca797baaa18009cbb2ebc13f9bebfe

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