slurm
AI infra interview questions tagged slurm, across every topic.
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Concepts behind "slurm"
The curriculum that explains the ideas these questions test.
Foundational
Slurm vs KubernetesEvery GPU platform team has this argument, and the two schedulers were built for different jobs: Slurm for long, large, all-or-nothing training on bare metal; Kubernetes for many services that scale up and down. Training fleets run Slurm because gang scheduling, topology and MPI-style launch are native there; serving fleets run Kubernetes because autoscaling and rolling deploys are native there. A platform that does both picks a hybrid: Slurm on Kubernetes (Slinky, Soperator) or a batch scheduler on Kubernetes (Kueue, Volcano, KAI). The interview question is which, for which workload, and why.🗂️ Scheduling & Orchestration
Foundational
Node Lifecycle: Drain, Upgrade and ReturnA node moves through a fixed cycle between provisioning and decommissioning, and most fleet operations are one lap around it: cordon so nothing new lands, drain so running work finishes or moves, act, validate, then return to the pool. The wall-clock cost of a fleet-wide change is dominated by draining rather than by the change itself, which makes the plan a scheduling document rather than a technical one.🗂️ Scheduling & Orchestration
Core
Slurm for AI ClustersSlurm is the scheduler most large training clusters still run, because it was built for exactly this shape of work: long jobs that need many nodes at once, launched with one command, placed with knowledge of the network. A candidate for a training-infrastructure role is expected to read an sbatch script, know how GPUs are requested and enforced, and explain why a job is stuck in the queue. This page covers the model, the commands that matter, the GPU-specific configuration, and the failure modes a platform engineer meets.🗂️ Scheduling & OrchestrationSign in