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AutoML has become a popular service that is provided by most leading cloudservice providers today. In this paper, we focus on the AutoML problem from the\emph{service provider's perspective}, motivated by the following practicalconsideration: When an AutoML service needs to serve {\em multiple users} with{\em multiple devices} at the same time, how can we allocate these devices tousers in an efficient way? We focus on GP-EI, one of the most popularalgorithms for automatic model selection and hyperparameter tuning, used bysystems such as Google Vizer. The technical contribution of this paper is thefirst multi-device, multi-tenant algorithm for GP-EI that is aware of\emph{multiple} computation devices and multiple users sharing the same set ofcomputation devices. Theoretically, given $N$ users and $M$ devices, we obtaina regret bound of $O((\text{\bf {MIU}}(T,K) + M)\frac{N^2}{M})$, where$\text{\bf {MIU}}(T,K)$ refers to the maximal incremental uncertainty up totime $T$ for the covariance matrix $K$. Empirically, we evaluate our algorithmon two applications of automatic model selection, and show that our algorithmsignificantly outperforms the strategy of serving users independently.Moreover, when multiple computation devices are available, we achievenear-linear speedup when the number of users is much larger than the number ofdevices.

AutoML from Service Provider's Perspective: Multi-device, Multi-tenant Model Selection with GP-EI