Fast and flexible AutoML with learning guarantees.
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Eugen Hotaj and cweill Explicitly set evaluation_master if RunConfig is provided.
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AdaNet is a lightweight and scalable TensorFlow AutoML framework for training and deploying adaptive neural networks using the AdaNet algorithm [Cortes et al. ICML 2017]. AdaNet combines several learned subnetworks in order to mitigate the complexity inherent in designing effective neural networks.


This is not an official Google product.

Getting Started

To get you started:


Requires Python 2.7, 3.4, 3.5, or 3.6.

adanet depends on bug fixes and enhancements not present in TensorFlow releases prior to 1.9. You must install or upgrade your TensorFlow package to at least 1.9:

$ pip install "tensorflow>=1.9.0"

Installing with Pip

You can use the pip package manager to install the official adanet package from PyPi:

$ pip install adanet

Installing from source

To install from source first you'll need to install bazel following their installation instructions.

Next clone adanet and cd into its root directory:

$ git clone && cd adanet

From the adanet root directory run the tests:

$ cd adanet
$ bazel test -c opt //...

Once you have verified that everything works well, install adanet as a pip package .

You are now ready to experiment with adanet.

import adanet


AdaNet is released under the Apache License 2.0.