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Finding the best VM configuration is key to achieve lower cost and higherthroughput, two primary concerns in cloud-based distributed neural network (NN)training today. Optimal VM selection that meets user constraints requiresefficiently navigating a large search space while controlling for theperformance variance associated with sharing cloud instances and networks. Inthis work, we characterize this variance in the context of distributed NNtraining and present results of a comprehensive throughput and cost-efficiencystudy we conducted across a wide array of instances to prune for the optimal VMsearch space. Using insights from these studies, we built Srifty, a system thatcombines runtime profiling with learned performance models to accuratelypredict training performance and find the best VM choice that satisfies userconstraints, potentially leveraging both heterogeneous setups and spotinstances. We integrated Srifty with PyTorch and evaluated it on Amazon EC2. Weconducted a large-scale generalization study of Srifty across more than 2Ktraining setups on EC2. Our results show that Srifty achieves an iterationlatency prediction error of 8%, and its VM instance recommendations offersignificant throughput gain and cost reduction while satisfying userconstraints compared to existing solutions in complex, real-world scenarios.

Srifty: Swift and Thrifty Distributed Training on the Cloud