Snap Machine Learning
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We describe an efficient, scalable machine learning library that enables very fast training of generalized linear models. We demonstrate that our library can remove the training time as a bottleneck for machine learning workloads, opening the door to a range of new applications. For instance, it allows more agile development, faster and more fine-grained exploration of the hyper-parameter space, enables scaling to massive datasets and makes frequent re-training of models possible in order to adapt to events as they occur. Our library, named Snap Machine Learning (Snap ML), combines recent advances in machine learning systems and algorithms in a nested manner to reflect the hierarchical architecture of modern distributed systems. This allows us to effectively leverage available network, memory and heterogeneous compute resources. On a terabyte-scale publicly available dataset for click-through-rate prediction in computational advertising, we demonstrate the training of a logistic regression classifier in 1.53 minutes, a 46x improvement over the fastest reported performance.
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