autogluon 0.0.14b20200830

AutoML Toolkit with MXNet Gluon

AutoML Toolkit for Deep Learning

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 deep learning models on tabular, image, and text data.

Example

# First install package from terminal:  pip install mxnet autogluon

from autogluon import TabularPrediction as task
train_data = task.Dataset(file_path='https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv')
test_data = task.Dataset(file_path='https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv')
predictor = task.fit(train_data=train_data, label='class')
performance = predictor.evaluate(test_data)

Resources

See the AutoGluon Website for documentation and instructions on:

• Installing AutoGluon
• Learning with tabular data
  • Tips to maximize accuracy
• Learning with image data
• Learning with text data
• More advanced topics such as Neural Architecture Search

Scientific Publications

• AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data (Arxiv, 2020)

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)

Train/Deploy AutoGluon in the Cloud

• AutoGluon-Tabular on AWS Marketplace
• Notebook: Running AutoGluon-Tabular 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}
}

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

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}
}

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.