AWS, Microsoft launch deep learning library

The Gluon interface now works with Apache MXNet and will also support Microsoft Cognitive Toolkit in an upcoming release.

While the interface primarily targets developers new to machine learning, the partners added that Gluon would also allow data veterans to leverage dynamic neural network graphs to build and train new models without sacrificing speed, Matt Wood, AWS general manager for AI, noted in a blog post.

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Simple and concise Microsoft and Amazon might be fierce rivals in the cloud space but the two companies have a history in working together at the edges of research.

The Gluon interface, announced in a joint press release on Thursday, lets developers use a Python API and pre-built neural network components to build machine learning models. Developers can use the Gluon interface to create neural networks on the fly, and to change their size and shape dynamically. This gives developers an interface to the AI that's easier to understand, letting them focus on designing the model. Aimed at programmers "of all skill levels", it enables new machine learning models to be created with "simple, concise" Python code. "We created the Gluon interface so building neural networks and training models can be as easy as building an app". The Seattle-based companies are join forces to make deep learning accessible to all developers with the new deep library. That's particularly important for increasingly complex models, since the calculations needed to both train and execute a neural network can take significant amounts of compute power. AWS and Microsoft published Gluon's reference specification so other deep learning engines can be integrated with the interface. "Today's reality is that building and training machine learning models require a great deal of heavy lifting and specialized expertise", said Swami Sivasubramanian, vice president of Amazon AI. Gluon, which is available as an open source project, will provide a shared set of building blocks that people can use with both Amazon and Microsoft's preferred machine learning frameworks. Other deep learning tools make model-building easier, but this simplicity can come at the cost of slower training performance. In-depth technical details are available here, and developers can now get started with Gluon by visiting this web page.

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