Gluon, a new open source deep learning interface intended to allow developers to more easily and quickly build machine learning models  has been announced in a joint statement from Amazon and Microsoft. 

Explaining the need for this interface, the announcement states:

Deep learning engines like Apache MXNet, Microsoft Cognitive Toolkit and TensorFlow have emerged to help optimize and speed the training process. However, these engines require developers to define the 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 — a concise, easy-to-understand programming interface that enables developers to quickly prototype and experiment with neural network models, and a training method that has minimal impact on the speed of the underlying engine. Developers can use the Gluon interface to create neural networks on the fly, and to change their size and shape dynamically. In addition, because the Gluon interface brings together the training algorithm and the neural network model, developers can perform model training one step at a time. This means it is much easier to debug, update and reuse neural networks.

According to its README on GitHub, where it is open sourced under an Apache 2 licence:

The Gluon API specification is an effort to improve speed, flexibility, and accessibility of deep learning technology for all developers, regardless of their deep learning framework of choice. The Gluon API offers a flexible interface that simplifies the process of prototyping, building, and training deep learning models without sacrificing training speed. It offers four distinct advantages: 

• Simple, Easy-to-Understand Code: Gluon offers a full set of plug-and-play neural network building blocks, including predefined layers, optimizers, and initializers.
  
  
• Flexible, Imperative Structure: Gluon does not require the neural network model to be rigidly defined, but rather brings the training algorithm and model closer together to provide flexibility in the development process.
  
  
• Dynamic Graphs: Gluon enables developers to define neural network models that are dynamic, meaning they can be built on the fly, with any structure, and using any of Python’s native control flow.
  
  
• High Performance: Gluon provides all of the above benefits without impacting the training speed that the underlying engine provides.

The Gluon interface currently works with the deep learning framework Apache MXNet and will support Microsoft Cognitive Toolkit (CNTK) in an upcoming release. Gluon’s reference specification has been published so that other deep learning engines can be integrated with the interface. 

Explaining the open source nature of the project and the collaboration with Microsoft, Swami Sivasubramanian, VP of Amazon AI states:

“The potential of machine learning can only be realized if it is accessible to all developers. Today’s reality is that building and training machine learning models require a great deal of heavy lifting and specialized expertise. We created the Gluon interface so building neural networks and training models can be as easy as building an app. We look forward to our collaboration with Microsoft on continuing to evolve the Gluon interface for developers interested in making machine learning easier to use.”

Microsoft's corporate VP of Microsoft AI and Research, Eric Boyd commented on the alliance between AWS and Microsoft over Gluon with: 

"We believe it is important for the industry to work together and pool resources to build technology that benefits the broader community.  Machine learning has the ability to transform the way we work, interact and communicate. To make this happen we need to put the right tools in the right hands, and the Gluon interface is a step in this direction."

As we reported last month, Microsoft have already taken a similar step in which it joined forces with Facebook in another open source AI initiative, the  Open Neural Network Exchange (ONNX) format, which helps developers switch between different AI frameworks by providing an extensible computation graph model, as well as definitions of built-in operators and standard data types.

Microsoft and Amazon are already working together in the realm of AI - in August they embarked on a joint endeavour to enable their respective Cortana and Alexa Assistants to communicate with each other.

A factor in common with these joint ventures may be to try to keep ahead, abreast or at least in the same ballpark as Google, which with its search capabilities, TensorFlow and Deep Mind is in a position to dominate AI. On the other hand both Gluon and ONNX are extensible so Google isn't precluded from joining in. 

More Information

Gluon on GitHub

Amazon announcement - AWS and Microsoft Announce Gluon to Simplify Deep Learning for Developers

Mircrosoft announcement - AWS and Microsoft announce Gluon, making deep learning accessible to all developers

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