Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads

24 August 2017

Papers citing "Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads"

6 / 6 papers shown

Title
Introducing Ínside' Out of Distribution Teddy Lazebnik 31 1 0 05 Jul 2024
SHiFT: An Efficient, Flexible Search Engine for Transfer Learning Cédric Renggli Xiaozhe Yao Luka Kolar Luka Rimanic Ana Klimovic Ce Zhang OOD 43 4 0 04 Apr 2022
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition Yang Li Yu Shen Wentao Zhang Jiawei Jiang Bolin Ding ... Jingren Zhou Zhi-Xin Yang Wentao Wu Ce Zhang Bin Cui LRM 29 44 0 19 Jul 2021
Federated Learning on Non-IID Data Silos: An Experimental Study Yue Liu Yiqun Diao Quan Chen Bingsheng He FedML OOD 101 955 0 03 Feb 2021
Efficient Memory Management for GPU-based Deep Learning Systems Junzhe Zhang Sai-Ho Yeung Yao Shu Bingsheng He Wei Wang 24 41 0 19 Feb 2019
Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis Tal Ben-Nun Torsten Hoefler GNN 33 704 0 26 Feb 2018