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This tempting but unsafe shortcut is not a sufficient remediation for DCGM active single-node diagnostic suites: Treat any successful invocation of dcgmi as proof that long-run hardware diagnostics finished without errors. Observed scenario: An administrator runs the most intrusive DCGM suite on a busy GPU without reserving exclusive access. The governing mechanism is: DCGM diagnostic suites exercise hardware and software health more deeply than passive status queries, sometimes consuming substantial resources. The proportionate action is: Schedule supported active DCGM diagnostics for the relevant GPU class and investigate detailed failures. A direct check is: Inspect dcgmi diag suite results, entity IDs, error logs, driver state and conflicting running workloads. The acceptance condition is: All targeted healthy GPUs complete the approved test suite or receive a documented fault disposition.

This alternative is relevant to CUDA device query and kernel smoke tests but does not directly resolve the current DCGM active single-node diagnostic suites issue: Inspect deviceQuery or equivalent CUDA sample output, driver-runtime versions and kernel launch error codes. Observed scenario: An administrator runs the most intrusive DCGM suite on a busy GPU without reserving exclusive access.

This alternative is relevant to Multi-node NCCL repeated burn-in but does not directly resolve the current DCGM active single-node diagnostic suites issue: Run repeatable multi-node NCCL stress over representative durations, message sizes and full-rack participation. Observed scenario: An administrator runs the most intrusive DCGM suite on a busy GPU without reserving exclusive access.

This alternative is relevant to ClusterKit multi-node qualification workflow but does not directly resolve the current DCGM active single-node diagnostic suites issue: Run supported ClusterKit or equivalent multifaceted qualification across the intended node set and preserve evidence. Observed scenario: A machine boots and is declared healthy from nvidia-smi alone despite failing DCGM deployment tests.

This alternative is relevant to CUDA device query and kernel smoke tests but does not directly resolve the current DCGM active single-node diagnostic suites issue: Execute supported CUDA runtime initialization and minimal kernel tests on each scheduled accelerator. Observed scenario: A machine boots and is declared healthy from nvidia-smi alone despite failing DCGM deployment tests.

This tempting but unsafe shortcut is not a sufficient remediation for DCGM active single-node diagnostic suites: Treat any successful invocation of dcgmi as proof that long-run hardware diagnostics finished without errors. A machine boots and is declared healthy from nvidia-smi alone despite failing DCGM deployment tests.

This tempting but unsafe shortcut is not a sufficient remediation for DCGM active single-node diagnostic suites: Treat any successful invocation of dcgmi as proof that long-run hardware diagnostics finished without errors. A machine boots and is declared healthy from nvidia-smi alone despite failing DCGM deployment tests. DCGM diagnostic suites exercise hardware and software health more deeply than passive status queries, sometimes consuming substantial resources.

That option concerns GPU thermal power and memory single-node burn-in. The proportionate action is: Schedule supported active DCGM diagnostics for the relevant GPU class and investigate detailed failures.

That option concerns ClusterKit multi-node qualification workflow. The proportionate action is: Schedule supported active DCGM diagnostics for the relevant GPU class and investigate detailed failures.

This alternative is relevant to Multi-node NCCL repeated burn-in but does not directly resolve the current DCGM active single-node diagnostic suites issue: Inspect long-run NCCL logs, error rate, interconnect counters, rank liveness and timing variation. Observed scenario: A machine boots and is declared healthy from nvidia-smi alone despite failing DCGM deployment tests.

This tempting but unsafe shortcut is not a sufficient remediation for DCGM active single-node diagnostic suites: Treat any successful invocation of dcgmi as proof that long-run hardware diagnostics finished without errors. Observed scenario: A machine boots and is declared healthy from nvidia-smi alone despite failing DCGM deployment tests. The governing mechanism is: DCGM diagnostic suites exercise hardware and software health more deeply than passive status queries, sometimes consuming substantial resources. The proportionate action is: Schedule supported active DCGM diagnostics for the relevant GPU class and investigate detailed failures.

This alternative is relevant to CUDA device query and kernel smoke tests but does not directly resolve the current DCGM active single-node diagnostic suites issue: Inspect deviceQuery or equivalent CUDA sample output, driver-runtime versions and kernel launch error codes. Observed scenario: A machine boots and is declared healthy from nvidia-smi alone despite failing DCGM deployment tests.

This alternative is relevant to GPU thermal power and memory single-node burn-in but does not directly resolve the current DCGM active single-node diagnostic suites issue: The node sustains approved workload performance without unplanned throttling, errors or facility alarms. Observed scenario: A machine boots and is declared healthy from nvidia-smi alone despite failing DCGM deployment tests.

This tempting but unsafe shortcut is not a sufficient remediation for DCGM active single-node diagnostic suites: Treat any successful invocation of dcgmi as proof that long-run hardware diagnostics finished without errors. Observed scenario: A machine boots and is declared healthy from nvidia-smi alone despite failing DCGM deployment tests. The governing mechanism is: DCGM diagnostic suites exercise hardware and software health more deeply than passive status queries, sometimes consuming substantial resources. The proportionate action is: Schedule supported active DCGM diagnostics for the relevant GPU class and investigate detailed failures. A direct check is: Inspect dcgmi diag suite results, entity IDs, error logs, driver state and conflicting running workloads. The acceptance condition is: All targeted healthy GPUs complete the approved test suite or receive a documented fault disposition.

This alternative is relevant to Multi-node NCCL repeated burn-in but does not directly resolve the current DCGM active single-node diagnostic suites issue: Every planned rank completes sustained correct collectives with throughput and error levels within acceptance limits. Observed scenario: A machine boots and is declared healthy from nvidia-smi alone despite failing DCGM deployment tests.

That option concerns Optical FEC BER validation during burn-in. Sustained accelerator load can reveal thermal and electrical limits not visible during idle or brief smoke checks.

This alternative is relevant to Multi-node NCCL repeated burn-in but does not directly resolve the current GPU thermal power and memory single-node burn-in issue: Run repeatable multi-node NCCL stress over representative durations, message sizes and full-rack participation. Observed scenario: A new accelerator stays cool at idle but throttles during a sustained tensor computation.

This tempting but unsafe shortcut is not a sufficient remediation for GPU thermal power and memory single-node burn-in: Conclude that a GPU is ready for training from its peak clock advertisement and one idle temperature sample.

This alternative is relevant to Inter-node NCCL east-west RDMA readiness but does not directly resolve the current GPU thermal power and memory single-node burn-in issue: Local NVLink health does not establish end-to-end east-west cluster communication across NICs and switches. Observed scenario: A new accelerator stays cool at idle but throttles during a sustained tensor computation.

That option concerns NeMo distributed training end-to-end burn-in. The acceptance condition is: The node sustains approved workload performance without unplanned throttling, errors or facility alarms.

This tempting but unsafe shortcut is not a sufficient remediation for GPU thermal power and memory single-node burn-in: Conclude that a GPU is ready for training from its peak clock advertisement and one idle temperature sample. Sustained accelerator load can reveal thermal and electrical limits not visible during idle or brief smoke checks.

This alternative is relevant to ClusterKit multi-node qualification workflow but does not directly resolve the current GPU thermal power and memory single-node burn-in issue: Inspect ClusterKit test plan, selected node identities, deployment versions, per-node diagnostics and failures. Observed scenario: A new accelerator stays cool at idle but throttles during a sustained tensor computation.

This tempting but unsafe shortcut is not a sufficient remediation for GPU thermal power and memory single-node burn-in: Conclude that a GPU is ready for training from its peak clock advertisement and one idle temperature sample. A new accelerator stays cool at idle but throttles during a sustained tensor computation.

This alternative is relevant to Long-duration burn-in schedule and symptom correlation but does not directly resolve the current GPU thermal power and memory single-node burn-in issue: Inspect burn-in start and stop times, GPU thermals, errors, link counters, competing workloads and incident timeline. Observed scenario: A new accelerator stays cool at idle but throttles during a sustained tensor computation.

This tempting but unsafe shortcut is not a sufficient remediation for GPU thermal power and memory single-node burn-in: Conclude that a GPU is ready for training from its peak clock advertisement and one idle temperature sample. A new accelerator stays cool at idle but throttles during a sustained tensor computation. Sustained accelerator load can reveal thermal and electrical limits not visible during idle or brief smoke checks.

This alternative is relevant to Sustained HPL burn-in thermal and numerical health but does not directly resolve the current GPU thermal power and memory single-node burn-in issue: The cluster completes the approved continuous HPL window with valid results and stable sustained performance. Observed scenario: A new accelerator stays cool at idle but throttles during a sustained tensor computation.

This alternative is relevant to DCGM active single-node diagnostic suites but does not directly resolve the current GPU thermal power and memory single-node burn-in issue: All targeted healthy GPUs complete the approved test suite or receive a documented fault disposition. Observed scenario: A new accelerator stays cool at idle but throttles during a sustained tensor computation.

This alternative is relevant to DCGM active single-node diagnostic suites but does not directly resolve the current GPU thermal power and memory single-node burn-in issue: Schedule supported active DCGM diagnostics for the relevant GPU class and investigate detailed failures. Observed scenario: A server passes a 15-second smoke test yet memory errors appear after prolonged workload stress.

This tempting but unsafe shortcut is not a sufficient remediation for GPU thermal power and memory single-node burn-in: Conclude that a GPU is ready for training from its peak clock advertisement and one idle temperature sample. A server passes a 15-second smoke test yet memory errors appear after prolonged workload stress.

This alternative is relevant to Inter-node NCCL east-west RDMA readiness but does not directly resolve the current GPU thermal power and memory single-node burn-in issue: Validate intended inter-node RDMA devices, transport selection and network reachability with multi-node NCCL tests. Observed scenario: A server passes a 15-second smoke test yet memory errors appear after prolonged workload stress.

This tempting but unsafe shortcut is not a sufficient remediation for GPU thermal power and memory single-node burn-in: Conclude that a GPU is ready for training from its peak clock advertisement and one idle temperature sample. A server passes a 15-second smoke test yet memory errors appear after prolonged workload stress. Sustained accelerator load can reveal thermal and electrical limits not visible during idle or brief smoke checks.

This alternative is relevant to Long-duration burn-in schedule and symptom correlation but does not directly resolve the current GPU thermal power and memory single-node burn-in issue: Inspect burn-in start and stop times, GPU thermals, errors, link counters, competing workloads and incident timeline. Observed scenario: A server passes a 15-second smoke test yet memory errors appear after prolonged workload stress.

This alternative is relevant to NeMo distributed training end-to-end burn-in but does not directly resolve the current GPU thermal power and memory single-node burn-in issue: Run a representative NeMo or equivalent distributed training smoke and burn-in covering data, communication and checkpoints. Observed scenario: A server passes a 15-second smoke test yet memory errors appear after prolonged workload stress.