How Can We Train Deep Learning Models Across Clouds and Continents? An Experimental Study
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Training deep learning models in the cloud or on dedicated hardware is expensive. A more cost-efficient option are hyperscale clouds offering spot instances, a cheap but ephemeral alternative to on-demand resources. As spot instance availability can change depending on the time of day, continent, and cloud provider, it could be more cost-efficient to distribute resources over the world. Still, it has not been investigated whether geo-distributed, data-parallel spot deep learning training could be a more cost-efficient alternative to centralized training. This paper aims to answer the question: Can deep learning models be cost-efficiently trained on a global market of spot VMs spanning different data centers and cloud providers? To provide guidance, we extensively evaluate the cost and throughput implications of training in different zones, continents, and clouds for representative CV and NLP models. To expand the current training options further, we compare the scalability potential for hybrid-cloud scenarios by adding cloud resources to on-premise hardware to improve training throughput. Finally, we show how leveraging spot instance pricing enables a new cost-efficient way to train models with multiple cheap VMs, trumping both more centralized and powerful hardware and even on-demand cloud offerings at competitive prices.
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