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The FastML project develops a distributed training framework for Deep Neural Networks that minimizes overheads in parallelization, enhancing computational efficiency. By leveraging advanced algorithmic techniques, it aims to lower costs and barriers for training accurate machine learning models in various industry appl…
Deep Learning is an area of massive progress, with myriad applications and significant industry adoption. A key enabler of its progress is the ability to train large, highly-accurate Deep Neural Networks (DNNs) in a distributed fashion, across tens to thousands of different computational nodes.
Yet, DNN training at scale poses severe challenges to standard paradigms in distributed computing; existing distributed training approaches and their practical implementations, via training libraries such…
INSTITUTE OF SCIENCE AND TECHNOLOGY AUSTRIA
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