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Microsoft Makes Its CNTK Deep Learning Software More Accessible To Developers By Posting It On GitHub

26 January 2016, 6:06 am EST By Vincent Lanaria Tech Times
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Microsoft has moved its Computational Network Toolkit (CNTK) deep-learning software from CodePlex to GitHub, providing access to the same resources it uses to other developers.  ( Christopher Pearce | Getty Images )

Microsoft announced that it is transferring its Computational Network Toolkit (CNTK) from CodePlex to the well-known open-source host GitHub, making it more accessible to many developers.

CNTK is an open-source deep-learning toolkit that became available back in April 2015. However, when it was still on CodePlex, it was restricted by an academic license, which means that it was virtually unused beyond scholarly use. By uploading the project on GitHub, Microsoft will be able to get more developers on the action.

"It is our hope that the community will take advantage of CNTK to share ideas more quickly through the exchange of open source working code," Microsoft's upload on GitHub reads.

Performance-wise, CNTK outdid four other computational toolkits that are used to make deep-learning models for speech and image recognition and other applications. According to Chief Scientist of Speech Research and Development at Microsoft Xuedong Huang, the toolkit now has more capable communication capacities.

"The CNTK toolkit is just insanely more efficient than anything we have ever seen," Huang says.

In the internal tests, CNTK was compared with the likes of Theano, Torch 7, Caffe and TensorFlow, Google's machine-learning system that was recently open-sourced.

(Photo : Microsoft)

The group of researchers published a more detailed document [pdf] regarding the benchmark results back in December 2015.

The company says that one of the reasons why it has the upper hand against others is because it can run on a single core or on a "large cluster of GPU-based computers," according to Microsoft Principal Development Manager Chris Basoglu, who's involved with the toolkit. More to the point, the researchers argue that "it can scale across more GPU-based machines than other publicly available toolkits," which offers a huge advantage when it comes to large-scale experiments or computations.

The team discovered that GPU is efficient in processing algorithms used in technology that can recognize images and movements as well as speak, hear and comprehend speech. As such, Microsoft uses powerful computers with impressive GPUs to run CNTK internally. For example, the company's popular virtual assistant Cortana takes advantage of CNTK for speech recognition.

"We further introduce the computational network toolkit (CNTK), an implementation of CN that supports both GPU and CPU. We describe the architecture and the key components of the CNTK, the command line options to use CNTK, and the network definition and model editing language, and provide sample setups for acoustic model, language model, and spoken language understanding," the team says [pdf] in a publication.

While it's important for Huang and his team to deliver the internal needs of Microsoft via a tool such as CNTK, they also want other developers who are geared toward deep learning to have access to the same resources.

"The reason we did this is we want to give our users flexibility to make changes. That strengthens the ecosystem and the toolset we have," Huang tells VentureBeat.

With the latest development, it appears that the advancements in artificial intelligence will certainly have a better overall progress, as it encourages more researchers to join the mix.

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