AutoML library for deep learning
Under Apache License 2.0
By keras-team

python deep-learning machine-learning tensorflow keras automl automated-machine-learning neural-architecture-search autodl

Official Website:

AutoKeras: An AutoML system based on Keras.
It is developed by DATA Lab at Texas A&M University.
The goal of AutoKeras is to make machine learning accessible to everyone.

Learning resources

import autokeras as ak

clf = ak.ImageClassifier(), y_train)
results = clf.predict(x_test)


To install the package, please use the pip installation as follows:

pip3 install autokeras

Please follow the installation guide for more details.

Note: Currently, AutoKeras is only compatible with Python >= 3.5 and TensorFlow >= 2.3.0.

Stay Up-to-Date

You can also follow us on Twitter @autokeras for the latest news.

Subscribe to our email list to receive announcements.

Questions and Discussions

GitHub Discussions:
Ask your questions on our GitHub Discussions.
It is a forum hosted on GitHub. We will monitor and answer the questions there.

Instant Communications

Request an invitation.
Use the #autokeras channel for communication.

QQ Group:
Join our QQ group 1150366085. Password: akqqgroup

Online Meetings:
Join the online meeting Google group.
The calendar event will appear on your Google Calendar.

Contributing Code

We engage in keeping everything about AutoKeras open to the public.
Everyone can easily join as a developer.
Here is how we manage our project.

Please join our Slack and send Haifeng Jin a message.
Or drop by our online meetings and talk to us.
We will help you get started!

Refer to our Contributing Guide to learn the best practices.

Thank all the contributors!


We accept financial support on Open Collective.
Thank every backer for supporting us!

Cite this work

Haifeng Jin, Qingquan Song, and Xia Hu. "Auto-keras: An efficient neural architecture search system." Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2019. (Download)

Biblatex entry:

title={Auto-Keras: An Efficient Neural Architecture Search System},
author={Jin, Haifeng and Song, Qingquan and Hu, Xia},
booktitle={Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},


The authors gratefully acknowledge the D3M program of the Defense Advanced Research Projects Agency (DARPA) administered through AFRL contract FA8750-17-2-0116; the Texas A&M College of Engineering, and Texas A&M University.