Distributed TensorFlow Has Arrived

KDnuggets has taken seriously its role to keep up with the newest releases of major deep learning projects, and in the recent past we have seen landmark such releases from major technology giants and as well as universities and research labs. While Microsoft, Yahoo!, AMPLabs, and others have all contributed outstanding projects in their own right, the landscape was most impacted in November, 2015, with the release of what is now the most popular open source machine learning library on Github by a wide margin, Google's TensorFlow. I wrote in the early days after its release of my initial dissatisfaction with the project, based primarily on the lack of distributed training capabilities (especially given that such capabilities were directly alluded to in the accompanying whitepaper's title). There were also a few other - lesser - "issues" I had with it, but the central point of contention was that it was single node only. This original post was polarizing, with many people upset at my "dismissal" of the tech powerhouse's latest offering (a closer read would reveal that I did not, in any way, dismiss it).