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This tempting but unsafe shortcut is not a sufficient remediation for Slurm GPU GRES enumeration and device mapping: Assume GPU count in nvidia-smi automatically updates Slurm resource declarations and MIG mappings. Observed scenario: A system enables MIG but leaves Slurm advertising one full physical GPU to each requesting tenant. The governing mechanism is: Schedulers allocate declared GPU resources; a mismatch between configured GRES and detected devices causes wrong placement. The proportionate action is: Synchronize Slurm GRES inventory with hardware and MIG state and validate per-job device isolation. A direct check is: Inspect slurmd discovery, gres configuration, CUDA_VISIBLE_DEVICES and simultaneous job placement. The acceptance condition is: Two concurrent test jobs receive only their intended available GPU devices without oversubscription.

This alternative is relevant to Base Command Manager head-node installation but does not directly resolve the current Slurm GPU GRES enumeration and device mapping issue: Install the supported BCM head-node services and preserve configuration data before provisioning and accepting workers. Observed scenario: A system enables MIG but leaves Slurm advertising one full physical GPU to each requesting tenant.

That option concerns Pyxis integration with Slurm jobs. Schedulers allocate declared GPU resources; a mismatch between configured GRES and detected devices causes wrong placement.

This alternative is relevant to Slurm GPU GRES enumeration and device mapping but does not directly resolve the current Slurm scheduling time limits fairness and preemption issue: Synchronize Slurm GRES inventory with hardware and MIG state and validate per-job device isolation. Observed scenario: Short validation jobs starve behind unlimited long-running work despite published queue priorities.

This alternative is relevant to BCM node categories and role inheritance but does not directly resolve the current Slurm scheduling time limits fairness and preemption issue: Define explicit BCM node categories and managed overrides for shared hardware and service configuration. Observed scenario: Short validation jobs starve behind unlimited long-running work despite published queue priorities.

This tempting but unsafe shortcut is not a sufficient remediation for Slurm scheduling time limits fairness and preemption: Raise every job's priority to maximum and assume the scheduler will resolve contention fairly without policy.

This tempting but unsafe shortcut is not a sufficient remediation for Slurm scheduling time limits fairness and preemption: Raise every job's priority to maximum and assume the scheduler will resolve contention fairly without policy. Slurm scheduling fairness and maintenance isolation require deliberate policy beyond raw accelerator count.

This alternative is relevant to BCM node categories and role inheritance but does not directly resolve the current Slurm scheduling time limits fairness and preemption issue: A BCM category groups settings applied to member nodes; untracked manual changes may conflict with managed desired state. Observed scenario: Short validation jobs starve behind unlimited long-running work despite published queue priorities.

That option concerns Slurm GPU GRES enumeration and device mapping. A direct check is: Inspect partition time limits, QOS priorities, pending reasons, preemption settings and maintenance reservations.

This alternative is relevant to BCM node categories and role inheritance but does not directly resolve the current Slurm scheduling time limits fairness and preemption issue: Inspect category membership, inherited images, node overrides, resulting configuration and change impact. Observed scenario: Short validation jobs starve behind unlimited long-running work despite published queue priorities.

This tempting but unsafe shortcut is not a sufficient remediation for Slurm scheduling time limits fairness and preemption: Raise every job's priority to maximum and assume the scheduler will resolve contention fairly without policy. Short validation jobs starve behind unlimited long-running work despite published queue priorities.

That option concerns Slurm controller partition setup. Slurm scheduling fairness and maintenance isolation require deliberate policy beyond raw accelerator count.

This alternative is relevant to BCM image provisioning and PXE worker boot but does not directly resolve the current Slurm scheduling time limits fairness and preemption issue: The intended worker reliably reboots into the approved OS and image with the right management identity. Observed scenario: Short validation jobs starve behind unlimited long-running work despite published queue priorities.

That option concerns Host SSH certificates keys and OS access segregation. Slurm scheduling fairness and maintenance isolation require deliberate policy beyond raw accelerator count.

This tempting but unsafe shortcut is not a sufficient remediation for Slurm scheduling time limits fairness and preemption: Raise every job's priority to maximum and assume the scheduler will resolve contention fairly without policy. Short validation jobs starve behind unlimited long-running work despite published queue priorities. Slurm scheduling fairness and maintenance isolation require deliberate policy beyond raw accelerator count.

This alternative is relevant to BCM high availability and management failover but does not directly resolve the current Slurm scheduling time limits fairness and preemption issue: Configure supported BCM HA peers, synchronized services and planned failover tests with clear recovery boundaries. Observed scenario: A team changes preemption settings and unexpectedly kills irreplaceable training without checkpoint policy.

This tempting but unsafe shortcut is not a sufficient remediation for Slurm scheduling time limits fairness and preemption: Raise every job's priority to maximum and assume the scheduler will resolve contention fairly without policy. A team changes preemption settings and unexpectedly kills irreplaceable training without checkpoint policy.

This alternative is relevant to Pyxis integration with Slurm jobs but does not directly resolve the current Slurm scheduling time limits fairness and preemption issue: Install supported Pyxis and Enroot plugin configuration on worker and controller paths and validate jobs end to end. Observed scenario: A team changes preemption settings and unexpectedly kills irreplaceable training without checkpoint policy.

That option concerns Host SSH certificates keys and OS access segregation. A direct check is: Inspect partition time limits, QOS priorities, pending reasons, preemption settings and maintenance reservations.

This tempting but unsafe shortcut is not a sufficient remediation for Slurm scheduling time limits fairness and preemption: Raise every job's priority to maximum and assume the scheduler will resolve contention fairly without policy. A team changes preemption settings and unexpectedly kills irreplaceable training without checkpoint policy. Slurm scheduling fairness and maintenance isolation require deliberate policy beyond raw accelerator count.

This alternative is relevant to BCM node categories and role inheritance but does not directly resolve the current Slurm scheduling time limits fairness and preemption issue: A BCM category groups settings applied to member nodes; untracked manual changes may conflict with managed desired state. Observed scenario: A team changes preemption settings and unexpectedly kills irreplaceable training without checkpoint policy.

This tempting but unsafe shortcut is not a sufficient remediation for Slurm scheduling time limits fairness and preemption: Raise every job's priority to maximum and assume the scheduler will resolve contention fairly without policy. Observed scenario: A team changes preemption settings and unexpectedly kills irreplaceable training without checkpoint policy. The governing mechanism is: Slurm scheduling fairness and maintenance isolation require deliberate policy beyond raw accelerator count. The proportionate action is: Define queue priority, reservations and preemption or checkpoint controls suited to workload importance.

This alternative is relevant to Pyxis integration with Slurm jobs but does not directly resolve the current Slurm scheduling time limits fairness and preemption issue: Inspect Slurm plugin configuration, Enroot availability, image identifiers, mount constraints and job logs. Observed scenario: A team changes preemption settings and unexpectedly kills irreplaceable training without checkpoint policy.

This alternative is relevant to BCM high availability and management failover but does not directly resolve the current Slurm scheduling time limits fairness and preemption issue: Check HA roles, sync status, virtual management endpoint, database replication and isolation tests. Observed scenario: A team changes preemption settings and unexpectedly kills irreplaceable training without checkpoint policy.

This tempting but unsafe shortcut is not a sufficient remediation for Slurm scheduling time limits fairness and preemption: Raise every job's priority to maximum and assume the scheduler will resolve contention fairly without policy. Observed scenario: A team changes preemption settings and unexpectedly kills irreplaceable training without checkpoint policy. The governing mechanism is: Slurm scheduling fairness and maintenance isolation require deliberate policy beyond raw accelerator count. The proportionate action is: Define queue priority, reservations and preemption or checkpoint controls suited to workload importance. A direct check is: Inspect partition time limits, QOS priorities, pending reasons, preemption settings and maintenance reservations. The acceptance condition is: Jobs follow approved priority and maintenance tests do not unexpectedly preempt protected production work.

This alternative is relevant to BCM high availability and management failover but does not directly resolve the current Slurm scheduling time limits fairness and preemption issue: A planned primary outage transfers management to standby without losing cluster configuration integrity. Observed scenario: A team changes preemption settings and unexpectedly kills irreplaceable training without checkpoint policy.

This alternative is relevant to BCM node categories and role inheritance but does not directly resolve the current Slurm scheduling time limits fairness and preemption issue: Correct GPU workers receive the intended profile without unrelated node categories being modified. Observed scenario: A team changes preemption settings and unexpectedly kills irreplaceable training without checkpoint policy.

This alternative is relevant to Linux operating system and kernel module support but does not directly resolve the current Slurm scheduling time limits fairness and preemption issue: Use the supported OS, kernel and package combination and validate driver modules on a representative node before rollout. Observed scenario: An operations schedule places rack burn-in tests in the same partition as customer training jobs.

This tempting but unsafe shortcut is not a sufficient remediation for Slurm scheduling time limits fairness and preemption: Raise every job's priority to maximum and assume the scheduler will resolve contention fairly without policy. An operations schedule places rack burn-in tests in the same partition as customer training jobs.

This alternative is relevant to Pyxis integration with Slurm jobs but does not directly resolve the current Slurm scheduling time limits fairness and preemption issue: Install supported Pyxis and Enroot plugin configuration on worker and controller paths and validate jobs end to end. Observed scenario: An operations schedule places rack burn-in tests in the same partition as customer training jobs.

This tempting but unsafe shortcut is not a sufficient remediation for Slurm scheduling time limits fairness and preemption: Raise every job's priority to maximum and assume the scheduler will resolve contention fairly without policy. An operations schedule places rack burn-in tests in the same partition as customer training jobs. Slurm scheduling fairness and maintenance isolation require deliberate policy beyond raw accelerator count.

This alternative is relevant to BCM high availability and management failover but does not directly resolve the current Slurm scheduling time limits fairness and preemption issue: Configure supported BCM HA peers, synchronized services and planned failover tests with clear recovery boundaries. Observed scenario: An operations schedule places rack burn-in tests in the same partition as customer training jobs.

This alternative is relevant to BCM node categories and role inheritance but does not directly resolve the current Slurm scheduling time limits fairness and preemption issue: Inspect category membership, inherited images, node overrides, resulting configuration and change impact. Observed scenario: An operations schedule places rack burn-in tests in the same partition as customer training jobs.