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Amazon Web Services Announces MXNet Is “Framework of Choice” for Deep Learning Using Julia and Other Languages

06 Dec 2016 | Julia Computing

Las Vegas, NV – Amazon Web Services announced at this week’s AWS re:Invent 2016 conference that MXNet is the framework of choice for deep learning using Julia and other languages.

According to Amazon.com CTO Werner Vogels, “In addition to scalability, MXNet offers the ability to both mix programming models (imperative and declarative), and code in a wide number of programming languages, including Python, C++, R, Scala, Julia, Matlab and JavaScript.” The full blog post is available here.

AWS provides the world’s largest cloud environment, which means that deep learning using Julia and MXNet is now available for more than 1 million businesses that use AWS.

Matt Wood, GM Product Strategy, Amazon Web Services
AWS re:Invent 2016
Photo by Greg Kelleher @gregkel

According to Matt Wood, GM Product Strategy for Amazon Web Services: “MXNet has a lot of the characteristics that developers like when they are going off and building deep learning.  First is programmability.  MXNet supports a really broad set of programming languages.  So whether you are used to using Python or Scala, or whether, like me, you are a fan of Julia or Javascript or Matlab or Go, you can use all of the languages you are used to using, and start running your deep learning straight away.”

Here’s Matt Wood talking about MXNet’s programmability and other nice features.

You can learn more about MXNet.jl by clicking here, or visit our Website to learn about Julia’s deep learning and GPU capabilities.

About Julia Computing and Julia

Julia Computing was founded in 2015 by the co-creators of the Julia language to provide support to businesses and researchers who use Julia, the fastest modern open source programming language for data and analytics.

Julia combines the functionality of quantitative environments such as Python and R with the speed of production programming languages like Java and C++ to solve big data and analytics problems. Julia delivers dramatic improvements in simplicity, speed, capacity and productivity.

Julia users and partners include: IBM, Intel, DARPA, Lawrence Berkeley National Laboratory, National Energy Research Scientific Computing Center (NERSC), Federal Aviation Administration (FAA), MIT Lincoln Labs, Moore Foundation, Nobel Laureate Thomas J. Sargent, Federal Reserve Bank of New York (FRBNY), Brazilian National Development Bank (BNDES), BlackRock, Conning, Berkery Noyes, BestX and many of the world’s largest investment banks, asset managers, fund managers, foreign exchange analysts, insurers, hedge funds and regulators. Julia is being used to analyze images of the universe and research dark matter, drive parallel computing on supercomputers, diagnose medical conditions, manage 3D printers, build drones, improve air safety, provide analytics for foreign exchange trading, insurance, regulatory compliance, macroeconomic modeling, sports analytics, manufacturing and much, much more.

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