Last November, Google opened up its in-house machine learning software TensorFlow, making the program that powers its translation services and photo analytics (among many other things) open-source and free to download. Now, the company is giving TensorFlow the machine learning equivalent of smart pills, releasing a distributed version of the software that allows it to run across multiple machines — up to hundreds at a time.
This sounds like an obvious way to improve TensorFlow, and, well, it is. Machine learning software only gets to be clever by analyzing large amounts of data — looking for common properties and trends like facial features in photographs, for example. Letting TensorFlow run these sorts of operations on networks of computers simultaneously rather than individual machines means users can make smarter systems, and improve them faster.
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This article originally appeared on Recode.net.
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