Dynamic control flow in large-scale machine learning
Dynamic control flow in large-scale machine learning Yu et al., EuroSys’18
(If you don’t have ACM Digital Library access, the paper can be accessed either by following the link above directly from The Morning Paper blog site).
In 2016 the Google Brain team published a paper giving an overview of TensorFlow, “TensorFlow: a system for large-scale machine learning.” This paper is a follow-up, taking a much deeper look at how TensorFlow supports dynamic control flow, including extending automatic differentiation to control flow constructs.
Embedding control flow within the dataflow graph
With a wide range of machine learning models in use, and rapid exploration of new techniques, a machine learning system needs to be expressive and flexible to support both research and production use cases. Given the ever larger models and training sets, a machine learning system also needs to be scalable. These means both using individual devices efficiently (anything from phones to custom ASCIs in datacenters), and also supporting parallel execution over multiple devices.
Both the building blocks of machine learning and the architectures built up using these blocks have been changing rapidly. This pace appears likely to continue. Therefore, rather than defining RNNs, MoEs Continue reading
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