Microsoft today announced that it is making it easier for developers to use its Computational Network Toolkit (CNTK) to build their own deep learning applications. The company first open sourced this toolkit in April 2015, but at the time, it was hosted on Microsoft’s own CodePlex site and was only available under a restrictive academic license. Now, the team is moving the project to GitHub and to the MIT open source license.
While Microsoft’s old license made the project accessible to academics, it wasn’t really geared toward production usage and tinkering outside of the academic environment. With this new license — and by having the project on GitHub — Microsoft hopes to attract other users as well.
As Microsoft’s chief speech scientist Xuedong Huang notes in today’s announcement, CNTK is highly optimized for speed. “The CNTK toolkit is just insanely more efficient than anything we have ever seen,” Huang said. Those other projects Huang is referring to include the likes of Google’s recently open-sourced TensorFlow, as well as projects like Torch, Theano and Caffe.
Microsoft argues that one of the advantages of CNTK is its ability to run on a single core, as well as on a large cluster of GPU-based machines. The company also says that it can scale across more machines than other projects (but that’s obviously a claim we can’t exactly verify).
It’s worth noting that Microsoft also quietly launched DMTK, another machine learning toolkit, last year. DMTK stands for “distributed machine learning toolkit” and it focuses on effectively analyzing very large amounts of data.
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