How Can We Train Deep Learning Models Across Clouds and Continents? An Experimental Study
This addresses the problem of high training costs for ML practitioners by exploring distributed cloud options, though it is incremental as it builds on existing spot VM and hybrid-cloud concepts.
This paper investigates whether deep learning models can be trained cost-efficiently using spot VMs across global data centers and cloud providers, finding that leveraging spot instance pricing enables training with multiple cheap VMs that outperforms centralized hardware and on-demand cloud offerings at competitive prices.
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, NLP, and ASR 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.