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The cloud is still a popular platform for distributed deep learning (DL)training jobs since resource sharing in the cloud can improve resourceutilization and reduce overall costs. However, such sharing also bringsmultiple challenges for DL training jobs, e.g., high-priority jobs couldimpact, even interrupt, low-priority jobs. Meanwhile, most existing distributedDL training systems require users to configure the resources (i.e., the numberof nodes and resources like CPU and memory allocated to each node) of jobsmanually before job submission and can not adjust the job's resources duringthe runtime. The resource configuration of a job deeply affect this job'sperformance (e.g., training throughput, resource utilization, and completionrate). However, this usually leads to poor performance of jobs since users failto provide optimal resource configuration in most cases. \system~is adistributed DL framework can auto-configure a DL job's initial resources anddynamically tune the job's resources to win the better performance. Withelastic capability, \system~can effectively adjusts the resources of a job whenthere are performance issues detected or a job fails because of faults oreviction. Evaluations results show \system~can outperform manual well-tunedresource configurations. Furthermore, in the production Kubernetes cluster of\company, \system~reduces the medium of job completion time by 31\%, andimproves the job completion rate by 6\%, CPU utilization by 15\%, and memoryutilization by 20\% compared with manual configuration.

DLRover: An Elastic Deep Training Extension with Auto Job Resource Recommendation