Posted On: Oct 12, 2017

Today Amazon Web Services and Microsoft announced a new deep learning library, called Gluon, which allows developers of all skill levels to prototype, build, train and deploy sophisticated machine learning models for the cloud, devices at the edge and mobile apps.

With Gluon, developers can build machine learning models using a simple Python API and a range of pre-built, optimized neural network components. This makes it easier for developers to build neural networks using simple, concise code, without sacrificing training performance.

Most deep learning frameworks require developers to define models and algorithms up-front using lengthy, complex code that is difficult to change. Other deep learning tools make model-building easier, but this simplicity can come at the cost of slower training performance.

The Gluon interface gives developers the best of both worlds, offering the following benefits:

1. Simple, easy-to-understand code – You get a full set of plug-and-play building blocks to build and train neural networks. These include predefined layers, optimizers, and initializers.

2. Flexible Structure – Gluon brings together the neural network model and the training algorithm providing greater flexibility in the development process. This flexible structure makes your code intuitive and easy-to-debug, and opens the door for more advanced models.

3. Dynamic Graphs – With Gluon, you can build on the fly, with any structure you want, and using any of Python’s native control flow.

4. High Performance – You get all of these benefits without needing to sacrifice training speed.

The Gluon interface currently works with the deep learning framework Apache MXNet and will support Microsoft Cognitive Toolkit (CNTK) in an upcoming release. AWS and Microsoft published Gluon’s reference specification so other deep learning engines can be integrated with the interface. To get started with the Gluon interface, visit: