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
SEATTLE & REDMOND, Wash. -- (Business Wire)
Today, Amazon Web Services Inc. (AWS), an Amazon.com company (NASDAQ:
AMZN), and Microsoft Corp. (NASDAQ: MSFT) announced 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.”
“We believe it is important for the industry to work together and pool
resources to build technology that benefits the broader community,” said
Eric Boyd, Corporate Vice President of Microsoft AI and Research. “This
is why Microsoft has collaborated with AWS to create the Gluon interface
and enable an open AI ecosystem where developers have freedom of choice.
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.”
"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 Andrew
Moore, dean of the School of Computer Science at Carnegie Mellon
University. “I really appreciate how the Gluon interface is able to keep
the code complexity at the same level as the concept; it’s a welcome
addition to the machine learning community."
“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 Georgia Institute of Technology.
“The Gluon interface dramatically accelerates the pace at which students
can pick up, apply, and innovate on new applications of machine
learning. The documentation is great, and I’m looking forward to
teaching it as part of my computer science course and in seminars that
focus on teaching cutting edge machine learning concepts across
different cities in the US.”
“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 Takero Ibuki, Senior Research Engineer at DOCOMO
Innovations. “The efficiency and flexibility this interface provides
will enable our teams to be more agile and experiment in ways that would
have required a prohibitive time investment in the past.”
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/.
About Amazon Web Services
For 11 years, Amazon Web Services has been the world’s most
comprehensive and broadly adopted cloud platform. AWS offers over 90
fully featured services for compute, storage, networking, database,
analytics, application services, deployment, management, developer,
mobile, Internet of Things (IoT), Artificial Intelligence (AI),
security, hybrid and enterprise applications, from 44 Availability Zones
(AZs) across 16 geographic regions in the U.S., Australia, Brazil,
Canada, China, Germany, India, Ireland, Japan, Korea, Singapore, and the
UK. AWS services are trusted by millions of active customers around the
world — including the fastest-growing startups, largest enterprises, and
leading government agencies — to power their infrastructure, make them
more agile, and lower costs. To learn more about AWS, visit https://aws.amazon.com.
About Amazon
Amazon is guided by four principles: customer obsession rather than
competitor focus, passion for invention, commitment to operational
excellence, and long-term thinking. Customer reviews, 1-Click shopping,
personalized recommendations, Prime, Fulfillment by Amazon, AWS, Kindle
Direct Publishing, Kindle, Fire tablets, Fire TV, Amazon Echo, and Alexa
are some of the products and services pioneered by Amazon. For more
information, visit www.amazon.com/about
and follow @AmazonNews.
About Microsoft
Microsoft (Nasdaq “MSFT” @microsoft) is the leading platform and
productivity company for the mobile-first, cloud-first world, and its
mission is to empower every person and every organization on the planet
to achieve more.
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Source: Amazon Web Services Inc.
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