EDITOR’ S QUESTION
In the case of large-scale GPU workloads deployed in cloud environments, choosing the right orchestration tool is vital for resource and cost efficiency.
To simplify distributed, large-scale training projects, companies can automate the management of computing resources( such as GPUs) across clusters of machines. These can assign workloads to available GPUs, balance computing power across servers, offer scalability based on demand, monitor performance, and detect failures for smoother operations.
If a GPU server crashes, it is self-healing.
There are two main orchestration models – Kubernetes and Slurm – that can handle large-scale GPU projects efficiently and reduce the need for manual management or intervention.
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