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Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads by Li, Tian; Zhong, Jie; Liu, Ji; Wu, Wentao; Zhang, Ce is a scholarly article available to read on EtoBox.

What is Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads about?

We present ease.ml, a declarative machine learning service platform we built to support more than ten research groups outside the computer science departments at ETH Zurich for their machine learning needs. With ease.ml, a user defines the high-level schema of a machine learning application and submits the task via a Web interface. The system automatically deals with the rest, such as model selection and data movement. In this paper, we describe the ease.ml architecture and focus on a novel technical problem introduced by ease.ml regarding resource allocation. We ask, as a "service provider" that manages a shared cluster of machines among all our users running machine learning workloads, what is the resource allocation strategy that maximizes the global satisfaction of all our users? Resource allocation is a critical yet subtle issue in this multi-tenant scenario, as we have to balance between efficiency and fairness. We first formalize the problem that we call multi-tenant model selection, aiming for minimizing the total regret of all users running automatic model selection tasks. We then develop a novel algorithm that combines multi-armed bandits with Bayesian optimization and pr

Author

Li, Tian; Zhong, Jie; Liu, Ji; Wu, Wentao; Zhang, Ce

Published

2017

Language

EN