This release is a pre-release and may not be stable for production use.
AutoML for Text, Image, and Tabular Data
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
# 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==0.3.1
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 |  |
 |
| TextPredictor | |
|
| ImagePredictor | |
|
| ObjectDetector | |
|
News
Announcement for previous users: The AutoGluon codebase has been modularized into namespace 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 to install dependencies required for AutoGluon's computer vision tasks (and vice versa). Unfortunately this improvement required a minor API change (eg. instead of from 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 viewed for version 0.0.15 which is the last released version under the old API.
Resources
See the AutoGluon Website for documentation and instructions on:
-
- Tips to maximize accuracy (if benchmarking, make sure to run
fit()with argumentpresets='best_quality').
- Tips to maximize accuracy (if benchmarking, make sure to run
-
More advanced topics such as Neural Architecture Search
Scientific Publications
- AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data (Arxiv, 2020)
- Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation (NeurIPS, 2020)
- Multimodal AutoML on Structured Tables with Text Fields (ICML AutoML Workshop, 2021)
Articles
- AutoGluon 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 Tutorials
- From 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 Cloud
- AutoGluon-Tabular on AWS Marketplace
- Running AutoGluon-Tabular on Amazon SageMaker
- Running AutoGluon Image Classification on Amazon SageMaker
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 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 resources
- General introduction into HNAS
- Introduction into HNAS with AutoGluon
- Example notebook
- Example scripts for efficient multi-fidelity HNAS of PyTorch neural network models
Also 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 Optimization
AutoGluon includes an algorithm 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}
}
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 to get started.
1 maintainer
Metadata
1 maintainer
Release files for autogluon.vision 0.3.2b20211119
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| autogluon.vision-0.3.2b20211119.tar.gz | 31.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| autogluon.vision-0.3.2b20211119-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 70.4 kB
Release files / autogluon.vision-0.3.2b20211119.tar.gz
| Download URL | autogluon.vision-0.3.2b20211119.tar.gz |
|---|---|
| Size | 31.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
d2ed711421cc2ef24775740ab367e2769d4687f850e3edb5d926bf0fc2aad633
|
|
BLAKE2b-256 checksum How to use checksums |
2936ba88367f96f6611959136ee1bd78b25e4c22d3996733898f9673436e5012
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.0 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.7.12
|
Release files / autogluon.vision-0.3.2b20211119-py3-none-any.whl
| Download URL | autogluon.vision-0.3.2b20211119-py3-none-any.whl |
|---|---|
| Size | 39.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
9532377dd702f78af5101c58d4ab1ba3171a1b8e62e62e2c9de8101f3dbc2d29
|
|
BLAKE2b-256 checksum How to use checksums |
84ae2e1c7a90992d5303dc30352f3f6089737c9be765d5d712704d81f035717d
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.8.0 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.7.12
|
