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
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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