标题： 在易失性实例上的机器学习 显示英文标题

标题： Machine Learning on Volatile Instances

摘要： 由于当前机器学习中使用的神经网络模型和训练数据集规模庞大，必须通过将梯度评估等任务分配到多个工作节点上来分布式地执行随机梯步下降（SGD）。 然而，运行分布式SGD可能非常昂贵，因为它可能需要长时间使用GPU等专用计算资源。 我们提出了成本效益高的策略，以利用比标准实例更便宜但可能被更高优先级的工作负载中断的易失性云实例。 据我们所知，这项工作是第一个量化主动工作节点数量的变化（由于抢占）对SGD收敛和训练时间影响的研究。 通过理解实例抢占概率、准确性和训练时间之间的权衡，我们能够推导出在易失性实例（如Amazon EC2 spot实例和其他可抢占云实例）上配置分布式SGD作业的实用策略。 实验结果表明，我们的策略在显著降低成本的情况下实现了良好的训练性能。 显示英文摘要

摘要： Due to the massive size of the neural network models and training datasets used in machine learning today, it is imperative to distribute stochastic gradient descent (SGD) by splitting up tasks such as gradient evaluation across multiple worker nodes. However, running distributed SGD can be prohibitively expensive because it may require specialized computing resources such as GPUs for extended periods of time. We propose cost-effective strategies to exploit volatile cloud instances that are cheaper than standard instances, but may be interrupted by higher priority workloads. To the best of our knowledge, this work is the first to quantify how variations in the number of active worker nodes (as a result of preemption) affects SGD convergence and the time to train the model. By understanding these trade-offs between preemption probability of the instances, accuracy, and training time, we are able to derive practical strategies for configuring distributed SGD jobs on volatile instances such as Amazon EC2 spot instances and other preemptible cloud instances. Experimental results show that our strategies achieve good training performance at substantially lower cost.