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DNN models across many domains continue to grow in size, resulting in highresource requirements for effective training, and unpalatable (and oftenunaffordable) costs for organizations and research labs across scales. Thispaper aims to significantly reduce training costs with effective use ofpreemptible instances, i.e., those that can be obtained at a much cheaper pricewhile idle, but may be preempted whenever requested by priority users. Doingso, however, requires new forms of resiliency and efficiency to cope with thepossibility of frequent preemptions - a failure model that is drasticallydifferent from the occasional failures in normal cluster settings that existingcheckpointing techniques target.  We present Bamboo, a distributed system that tackles these challenges byintroducing redundant computations into the training pipeline, i.e., wherebyone node performs computations over not only its own layers but also over somelayers in its neighbor. Our key insight is that training large models oftenrequires pipeline parallelism where "pipeline bubbles" naturally exist. Bamboocarefully fills redundant computations into these bubbles, providing resilienceat a low cost. Across a variety of widely used DNN models, Bamboo outperformstraditional checkpointing by 3.7x in training throughput, and reduces costs by2.4x compared to a setting where on-demand instances are used.

Bamboo: Making Preemptible Instances Resilient for Affordable Training of Large DNNs