- 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
Amazon Web Services Inc., and Microsoft Corp. announced yesterday a new deep learning library, called Gluon, that 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. The Gluon interface currently works with Apache MXNet and will support Microsoft Cognitive Toolkit (CNTK) in an upcoming release. With the Gluon interface, 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 of all skill levels to build neural networks using simple, concise code, without sacrificing performance. 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: https://github.com/gluon-api/gluon-api/.
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 collaboration with Microsoft on continuing to evolve the Gluon interface for developers interested in making machine learning easier to use.”
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/.