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FastML: Efficient and Cost-Effective Distributed Machine Learning

CORDIS · observation · Publication date unknown

FastML: Efficient and Cost-Effective Distributed Machine Learning 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 as PyTorch or TensorFlow, often suffer from major distribution bottlenecks, which can significantly reduce computational efficiency, leading to wasted time, money, and energy. The FastML proof-of-concept (PoC) project will tackle this efficiency challenge head-on, by introducing a distributed training framework that will significantly reduce or even eliminate the overheads of pa

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recordType

award

status

SIGNED

region

EU

value

150000

unit

EUR