Function-as-a-Service (FaaS) has raised a growing interest in how to "tame" serverless computing to enable domain-specific use cases such as data-intensive applications and machine learning (ML), to name a few. Recently, several systems have been implemented for training ML models. Certainly, these research articles are significant steps in the correct direction. However, they do not completely answer the nagging question of when serverless ML training can be more cost-effective compared to traditional "serverful" computing. To help in this endeavor, we propose MLLess, a FaaS-based ML training prototype built atop IBM Cloud Functions. To boost cost-efficiency, MLLess implements two innovative optimizations tailored to the traits of serverless computing: on one hand, a significance filter, to make indirect communication more effective, and on the other hand, a scale-in auto-tuner, to reduce cost by benefiting from the FaaS sub-second billing model (often per 100ms). Our results certify that MLLess can be 15X faster than serverful ML systems at a lower cost for sparse ML models that exhibit fast convergence such as sparse logistic regression and matrix factorization. Furthermore, our results show that MLLess can easily scale out to increasingly large fleets of serverless workers.
翻译:功能- A- Service( Faas- A- Service)( Faas- A- Service)( FaaS) 提高了人们对如何“ 调用” 服务器的无服务器计算方法的兴趣, 以使数据密集应用程序和机器学习(ML)等特定领域应用案例(ML) 成为其中几个例子。 最近, 实施了几个系统来培训 ML 模型。 当然, 这些研究文章是朝着正确方向迈出的重要步骤。 但是, 它们并没有完全解答这样一个棘手问题: 与传统的“ 保守” 计费模式相比, 没有服务器 MLL培训何时能更具成本效益? 为了帮助这项努力, 我们建议MLLess( 以FaAS为基础的ML ) 培训模型, 在 Oper IBMB Cloud Conful Concess( Offective- ML) 系统上建立。 为了提高成本效率, MLLess 实施两种创新的优化方法, 以适应没有服务器计算机的特性。 : 一方面, 重要过滤器, 使间接通讯更加有效;另一方面, 使自动图解缩模模的模型显示, 快速的机模模型能够快速地展示, 快速地显示无序的机模模模模。