Propose an architectural solution for contended GPU training resources
Tests multi-tenant GPU scheduling design at scale. Great answers tier jobs by checkpointability, apply quota-based preemption, mix spot and on-demand instances, and use MIG or time-slicing to bin-pack. Red flag: buying GPUs without scheduling logic.
WHAT IT TESTS: whether you can design a multi-tenant GPU scheduler that balances cost, fairness, and utilization across training and inference. ANSWER OUTLINE: segment workloads by priority and fault tolerance; enforce namespace quotas and preemption; integrate spot instances with checkpointing; adopt MIG or time-slicing for underutilized cards; use gang or topology-aware scheduling to reduce fragmentation. RED FLAG: proposing horizontal scaling or bigger instances without addressing bin-packing, preemption, or mixed workload orchestration.
Read the original → kubezilla.io
- #mlops
- #kubernetes
- #gpu-scheduling
- #distributed-systems
- #resource-management
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