|AWS and Microsoft Announce Gluon, Making Deep Learning Accessible to All Developers|
New open source deep learning interface allows developers to more easily and quickly build machine learning models without compromising training performance
Jointly developed reference specification makes it possible for Gluon to work with any deep learning engine; support for Apache MXNet available today and support for Microsoft Cognitive Toolkit coming soon
Developers build neural networks using three components: training data, a model and an algorithm. The algorithm trains the model to understand patterns in the data. Because the volume of data is large and the models and algorithms are complex, training a model often takes days or even weeks. 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.
"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 requires a great deal of heavy lifting
and specialized expertise,” said Swami Sivasubramanian, VP of Amazon AI.
“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
“We believe it is important for the industry to work together and pool
resources to build technology that benefits the broader community,” said
"FINRA is using deep learning tools to process the vast amount of data we collect in our data lake," said Saman Michael Far, Senior Vice President and CTO, FINRA. "We are excited about the new Gluon interface, which makes it easier to leverage the capabilities of Apache MXNet, an open source framework that aligns with FINRA’s strategy of embracing open source and cloud for machine learning on big data.”
"I rarely see software engineering abstraction principles and numerical
machine learning playing well together — and something that may look
good in a tutorial could be hundreds of lines of code,” said
“The Gluon interface solves the age old problem of having to choose
between ease-of-use and performance, and I know it will resonate with my
students,” said Nikolaos Vasiloglou, Adjunct Professor of Electrical
Engineering and Computer Science at
“We think the Gluon interface will be an important addition to our
machine learning toolkit because it makes it easy to prototype machine
learning models,” said
The Gluon interface is open source and available today in Apache MXNet 0.11, with support for Microsoft Cognitive Toolkit (CNTK) in an upcoming release. Developers can learn how to get started using Gluon with MXNet by viewing tutorials for both beginners and experts available by visiting: https://mxnet.incubator.apache.org/gluon/.
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