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Hyper: Distributed Cloud Processing for Large-Scale Deep Learning Tasks
[article]
2019
arXiv
pre-print
Training and deploying deep learning models in real-world applications require processing large amounts of data. This is a challenging task when the amount of data grows to a hundred terabytes, or even, petabyte-scale. We introduce a hybrid distributed cloud framework with a unified view to multiple clouds and an on-premise infrastructure for processing tasks using both CPU and GPU compute instances at scale. The system implements a distributed file system and failure-tolerant task processing
arXiv:1910.07172v1
fatcat:5qlpul5yqzfyrhfgallvnyj4ne