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That option concerns DCGM active single-node diagnostic suites. Cluster readiness is a multi-component property requiring simultaneous compute, host and network validation.

This tempting but unsafe shortcut is not a sufficient remediation for ClusterKit multi-node qualification workflow: Mark a cluster fully validated solely because one representative server completed a local benchmark. Observed scenario: A project reports ClusterKit completion without preserving versions, node list or failed test outputs. The governing mechanism is: Cluster readiness is a multi-component property requiring simultaneous compute, host and network validation. The proportionate action is: Run supported ClusterKit or equivalent multifaceted qualification across the intended node set and preserve evidence. A direct check is: Inspect ClusterKit test plan, selected node identities, deployment versions, per-node diagnostics and failures. The acceptance condition is: The specified node group completes its approved multifaceted readiness checks with traceable results.

This alternative is relevant to Intra-node NCCL all-reduce test scope but does not directly resolve the current ClusterKit multi-node qualification workflow issue: Run supported single-node NCCL collectives spanning the intended local GPUs and compare with baseline topology. Observed scenario: A project reports ClusterKit completion without preserving versions, node list or failed test outputs.

This alternative is relevant to Cluster-wide preflight hardware inventory but does not directly resolve the current Long-duration burn-in schedule and symptom correlation issue: Capture a reproducible preflight inventory of node hardware, software, connections and expected acceptance tests. Observed scenario: A rack passes a short test but fails with heat saturation three hours into repeated training load.

This alternative is relevant to Multi-node NCCL repeated burn-in but does not directly resolve the current Long-duration burn-in schedule and symptom correlation issue: Run repeatable multi-node NCCL stress over representative durations, message sizes and full-rack participation. Observed scenario: A rack passes a short test but fails with heat saturation three hours into repeated training load.

This tempting but unsafe shortcut is not a sufficient remediation for Long-duration burn-in schedule and symptom correlation: Accept a multi-hour burn-in from its final exit code alone while ignoring intermediate fatal events and throttling.

This tempting but unsafe shortcut is not a sufficient remediation for Long-duration burn-in schedule and symptom correlation: Accept a multi-hour burn-in from its final exit code alone while ignoring intermediate fatal events and throttling. Some thermal, power, memory and fabric failures appear only under sustained stress; complete evidence includes trends.

That option concerns Cluster-wide preflight hardware inventory. The acceptance condition is: The cluster maintains acceptable health and validated throughput for the required duration without hidden alarm accumulation.

That option concerns GPU thermal power and memory single-node burn-in. The acceptance condition is: The cluster maintains acceptable health and validated throughput for the required duration without hidden alarm accumulation.

This alternative is relevant to HPL configuration matrix dimensions and performance but does not directly resolve the current Long-duration burn-in schedule and symptom correlation issue: Inspect HPL configuration, problem size, process grid, elapsed time, residual report and resource mapping. Observed scenario: A rack passes a short test but fails with heat saturation three hours into repeated training load.

This alternative is relevant to GPU thermal power and memory single-node burn-in but does not directly resolve the current Long-duration burn-in schedule and symptom correlation issue: Inspect GPU temperature and power traces, clock throttle reasons, error counters and completed stress duration. Observed scenario: A rack passes a short test but fails with heat saturation three hours into repeated training load.

This tempting but unsafe shortcut is not a sufficient remediation for Long-duration burn-in schedule and symptom correlation: Accept a multi-hour burn-in from its final exit code alone while ignoring intermediate fatal events and throttling. A rack passes a short test but fails with heat saturation three hours into repeated training load.

This tempting but unsafe shortcut is not a sufficient remediation for Long-duration burn-in schedule and symptom correlation: Accept a multi-hour burn-in from its final exit code alone while ignoring intermediate fatal events and throttling. A rack passes a short test but fails with heat saturation three hours into repeated training load. Some thermal, power, memory and fabric failures appear only under sustained stress; complete evidence includes trends.

This alternative is relevant to GPU thermal power and memory single-node burn-in but does not directly resolve the current Long-duration burn-in schedule and symptom correlation issue: The node sustains approved workload performance without unplanned throttling, errors or facility alarms. Observed scenario: A rack passes a short test but fails with heat saturation three hours into repeated training load.

This alternative is relevant to HPL configuration matrix dimensions and performance but does not directly resolve the current Long-duration burn-in schedule and symptom correlation issue: The approved HPL run completes valid numerical checks with repeatable measured throughput documented. Observed scenario: A rack passes a short test but fails with heat saturation three hours into repeated training load.

This tempting but unsafe shortcut is not a sufficient remediation for Long-duration burn-in schedule and symptom correlation: Accept a multi-hour burn-in from its final exit code alone while ignoring intermediate fatal events and throttling. A burn-in job runs while user workloads compete for resources and its metrics become impossible to interpret.

This alternative is relevant to GPU thermal power and memory single-node burn-in but does not directly resolve the current Long-duration burn-in schedule and symptom correlation issue: Run a controlled representative GPU stress with monitoring of temperatures, memory and power-limit behavior. Observed scenario: A burn-in job runs while user workloads compete for resources and its metrics become impossible to interpret.

This alternative is relevant to Sustained HPL burn-in thermal and numerical health but does not directly resolve the current Long-duration burn-in schedule and symptom correlation issue: Execute sustained HPL trials with validated numerical results, platform telemetry and consistent power constraints. Observed scenario: A burn-in job runs while user workloads compete for resources and its metrics become impossible to interpret.

That option concerns HPL configuration matrix dimensions and performance. The acceptance condition is: The cluster maintains acceptable health and validated throughput for the required duration without hidden alarm accumulation.

That option concerns CUDA device query and kernel smoke tests. The acceptance condition is: The cluster maintains acceptable health and validated throughput for the required duration without hidden alarm accumulation.

This tempting but unsafe shortcut is not a sufficient remediation for Long-duration burn-in schedule and symptom correlation: Accept a multi-hour burn-in from its final exit code alone while ignoring intermediate fatal events and throttling. A burn-in job runs while user workloads compete for resources and its metrics become impossible to interpret. Some thermal, power, memory and fabric failures appear only under sustained stress; complete evidence includes trends.

This alternative is relevant to Sustained HPL burn-in thermal and numerical health but does not directly resolve the current Long-duration burn-in schedule and symptom correlation issue: Inspect per-iteration residual status, achieved FLOPS, GPU errors, thermal trend and job exit codes. Observed scenario: A burn-in job runs while user workloads compete for resources and its metrics become impossible to interpret.

This alternative is relevant to Multi-node NCCL repeated burn-in but does not directly resolve the current Long-duration burn-in schedule and symptom correlation issue: Inspect long-run NCCL logs, error rate, interconnect counters, rank liveness and timing variation. Observed scenario: A burn-in job runs while user workloads compete for resources and its metrics become impossible to interpret.

This tempting but unsafe shortcut is not a sufficient remediation for Long-duration burn-in schedule and symptom correlation: Accept a multi-hour burn-in from its final exit code alone while ignoring intermediate fatal events and throttling. Observed scenario: A burn-in job runs while user workloads compete for resources and its metrics become impossible to interpret. The governing mechanism is: Some thermal, power, memory and fabric failures appear only under sustained stress; complete evidence includes trends. The proportionate action is: Plan approved-duration coordinated burn-in with continuous telemetry and clean test resource reservations.

This alternative is relevant to Optical FEC BER validation during burn-in but does not directly resolve the current Long-duration burn-in schedule and symptom correlation issue: The link maintains vendor-supported signal margin and acceptable bit-error behavior during sustained traffic. Observed scenario: A burn-in job runs while user workloads compete for resources and its metrics become impossible to interpret.

This tempting but unsafe shortcut is not a sufficient remediation for Long-duration burn-in schedule and symptom correlation: Accept a multi-hour burn-in from its final exit code alone while ignoring intermediate fatal events and throttling. Observed scenario: A burn-in job runs while user workloads compete for resources and its metrics become impossible to interpret. The governing mechanism is: Some thermal, power, memory and fabric failures appear only under sustained stress; complete evidence includes trends. The proportionate action is: Plan approved-duration coordinated burn-in with continuous telemetry and clean test resource reservations. A direct check is: Inspect burn-in start and stop times, GPU thermals, errors, link counters, competing workloads and incident timeline. The acceptance condition is: The cluster maintains acceptable health and validated throughput for the required duration without hidden alarm accumulation.

This alternative is relevant to GPU thermal power and memory single-node burn-in but does not directly resolve the current Long-duration burn-in schedule and symptom correlation issue: The node sustains approved workload performance without unplanned throttling, errors or facility alarms. Observed scenario: A burn-in job runs while user workloads compete for resources and its metrics become impossible to interpret.

This alternative is relevant to GPU thermal power and memory single-node burn-in but does not directly resolve the current Long-duration burn-in schedule and symptom correlation issue: Run a controlled representative GPU stress with monitoring of temperatures, memory and power-limit behavior. Observed scenario: Operations records only final success and omits intermittent ECC or fabric alarms during a 24-hour test.

This tempting but unsafe shortcut is not a sufficient remediation for Long-duration burn-in schedule and symptom correlation: Accept a multi-hour burn-in from its final exit code alone while ignoring intermediate fatal events and throttling. Operations records only final success and omits intermittent ECC or fabric alarms during a 24-hour test.

This alternative is relevant to Cluster-wide preflight hardware inventory but does not directly resolve the current Long-duration burn-in schedule and symptom correlation issue: Capture a reproducible preflight inventory of node hardware, software, connections and expected acceptance tests. Observed scenario: Operations records only final success and omits intermittent ECC or fabric alarms during a 24-hour test.

This tempting but unsafe shortcut is not a sufficient remediation for Long-duration burn-in schedule and symptom correlation: Accept a multi-hour burn-in from its final exit code alone while ignoring intermediate fatal events and throttling. Operations records only final success and omits intermittent ECC or fabric alarms during a 24-hour test. Some thermal, power, memory and fabric failures appear only under sustained stress; complete evidence includes trends.

This alternative is relevant to Optical FEC BER validation during burn-in but does not directly resolve the current Long-duration burn-in schedule and symptom correlation issue: Collect optical power and FEC or BER data over representative load, then isolate unsafe optics or fibers. Observed scenario: Operations records only final success and omits intermittent ECC or fabric alarms during a 24-hour test.

That option concerns NeMo distributed training end-to-end burn-in. Some thermal, power, memory and fabric failures appear only under sustained stress; complete evidence includes trends.