AutoGluon: AutoML Toolkit for Deep Learning

AutoGluon enables easy-to-use and easy-to-extend AutoML with a focus on deep learning and real-world applications spanning image, text, or tabular data. Intended for both ML beginners and experts, AutoGluon enables you to:

• Quickly prototype deep learning solutions for your data with few lines of code.

• Leverage automatic hyperparameter tuning, model selection / architecture search, and data processing.

• Automatically utilize state-of-the-art deep learning techniques without expert knowledge.

• Easily improve existing bespoke models and data pipelines, or customize AutoGluon for your use-case.

Note

Example using AutoGluon to train and deploy high-performance model on a tabular dataset:

>>> from autogluon import TabularPrediction as task
>>> predictor = task.fit(train_data=task.Dataset(file_path=TRAIN_DATA.csv), label=COLUMN_NAME)
>>> predictions = predictor.predict(task.Dataset(file_path=TEST_DATA.csv))

AutoGluon can be applied just as easily for prediction tasks with image or text data.

Installation

Note

AutoGluon requires Python version 3.6 or 3.7. Linux is the only operating system fully supported for now (complete Mac OSX and Windows versions will be available soon). For troubleshooting the installation process, you can check the Installation FAQ.

Select your preferences below and run the corresponding install commands:

OS: Linux Mac

Linux.

Mac OSX.

Version: PIP Source

PIP Release.

Install AutoGluon from source.

Backend: CPU GPU

Build-in backend for CPU.

Required to run on Nvidia GPUs.

Command:

python3 -m pip install --upgrade "mxnet<2.0.0"
python3 -m pip install autogluon

# Here we assume CUDA 10.1 is installed.  You should change the number
# according to your own CUDA version (e.g. mxnet_cu100 for CUDA 10.0).
python3 -m pip install --upgrade "mxnet_cu101<2.0.0"
python3 -m pip install autogluon

python3 -m pip install --upgrade "mxnet<2.0.0"
git clone https://github.com/awslabs/autogluon
cd autogluon && python3 setup.py develop

# Here we assume CUDA 10.1 is installed.  You should change the number
# according to your own CUDA version (e.g. mxnet_cu102 for CUDA 10.2).
python3 -m pip install --upgrade "mxnet_cu101<2.0.0"
git clone https://github.com/awslabs/autogluon
cd autogluon && python3 setup.py develop

Note

If you don’t have them, please first install: XCode, Homebrew, LibOMP. Once you have Homebrew, LibOMP can be installed via:

brew install libomp

python3 -m pip install --upgrade "mxnet<2.0.0"
python3 -m pip install autogluon

Note

AutoGluon is not yet fully functional on Mac OSX. If you encounter MXNet system errors, please use Linux instead. However, you can currently use AutoGluon for less compute-intensive TabularPrediction tasks on your Mac laptop (but only with hyperparameter_tune = False).

Note

GPU usage is not yet supported on Mac OSX, please use Linux to utilize GPUs in AutoGluon.

Note

If you don’t have them, please first install: XCode, Homebrew, LibOMP. Once you have Homebrew, LibOMP can be installed via:

brew install libomp

python3 -m pip install --upgrade "mxnet<2.0.0"
git clone https://github.com/awslabs/autogluon
cd autogluon && python3 setup.py develop

Note

AutoGluon is not yet fully functional on Mac OSX. If you encounter MXNet system errors, please use Linux instead. However, you can currently use AutoGluon for less compute-intensive TabularPrediction tasks on your Mac laptop (but only with hyperparameter_tune = False).

Note

GPU usage is not yet supported on Mac OSX , please use Linux to utilize GPUs in AutoGluon.

Quick Start

Application	Illustration
Tabular Prediction: predict values in column of data table based on other columns' values
Image Classification: recognize the main object in an image
Object Detection: detect multiple objects with their bounding boxes in an image
Text Prediction: make predictions based on text content

Tutorials

Tabular Predictiontutorials/tabular_prediction/index.html

How to predict variables in tabular datasets.

Image Classificationtutorials/image_classification/index.html

How to classify images into various categories.

Object Detectiontutorials/object_detection/index.html

How to detect objects and their location in images.

Text Predictiontutorials/text_prediction/index.html

How to solve NLP problems via supervised learning from raw text.

Advanced Topics

Customize AutoGluontutorials/course/index.html

Advanced usage of AutoGluon APIs for customized applications.

Neural Architecture Searchtutorials/nas/index.html

How to perform neural architecture search.

For PyTorch Userstutorials/torch/index.html

How to do hyperparameter tuning or architecture search for any PyTorch model.