Distributed Deep Learning (DDL), as a paradigm, dictates the use of GPU-based clusters as the optimal infrastructure for training large-scale Deep Neural Networks (DNNs). However, the high cost of such resources makes them inaccessible to many users. Public cloud services, particularly Spot Virtual Machines (VMs), offer a cost-effective alternative, but their unpredictable availability poses a significant challenge to the crucial checkpointing process in DDL. To address this, we introduce DeepVM, a novel solution that recommends cost-effective cluster configurations by intelligently balancing the use of Spot and On-Demand VMs. DeepVM leverages a four-stage process that analyzes instance performance using the FLOPP (FLoating-point Operations Per Price) metric, performs architecture-level analysis with linear programming, and identifies the optimal configuration for the user-specific needs. Extensive simulations and real-world deployments in the AWS environment demonstrate that DeepVM consistently outperforms other policies, reducing training costs and overall makespan. By enabling cost-effective checkpointing with Spot VMs, DeepVM opens up DDL to a wider range of users and facilitates a more efficient training of complex DNNs.

翻译：分布式深度学习（DDL）作为一种范式，要求采用基于GPU的集群作为训练大规模深度神经网络（DNN）的最佳基础设施。然而，此类资源的高昂成本使得许多用户难以企及。公有云服务，尤其是竞价虚拟机（Spot VM），提供了一种高性价比的选择，但其不确定的可用性对DDL中至关重要的检查点机制构成了重大挑战。为此，我们提出DeepVM——一种通过智能平衡竞价与按需虚拟机使用策略来推荐高性价比集群配置的新方案。DeepVM采用四阶段流程：通过FLOPP（每价格浮点运算次数）指标分析实例性能，利用线性规划进行架构级分析，最终为用户特定需求识别最优配置。在AWS环境中的广泛仿真与现实部署表明，DeepVM始终优于其他策略，能够有效降低训练成本与整体完成时间。通过实现基于竞价虚拟机的高性价比检查点机制，DeepVM使更广泛的用户群体能够使用DDL，并推动复杂DNN的更高效训练。