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Reliable GPU-cloud operations require more than a GPU utilization chart. Correlate GPU health and DCGM events with node, Kubernetes, Slurm, application, network, storage, quota, and provider-maintenance signals. Track service availability separately from usable capacity, preserve a durable record for every job, and automate only bounded, auditable recovery actions.
Build a monitoring system that follows a GPU request end to end
A useful design has four cooperating layers: collectors, a time-series database, visualization, and alerting. NVIDIA describes this pattern for GPU telemetry. Prometheus can store metrics, Grafana can provide dashboards, and Alertmanager can route notifications. In Kubernetes, kube-state-metrics supplies object and scheduling state while node_exporter supplies operating-system signals. DCGM Exporter exposes NVIDIA GPU metrics to Prometheus and is deployed by default with the NVIDIA GPU Operator.
The important design choice is correlation. A GPU error that is not joined to its node, pod, job, checkpoint, and recent maintenance event is an isolated symptom rather than an explanation. Use a common event and metric identity so an investigator can move from a customer-visible failure to the exact GPU UUID and then to the surrounding system evidence.
Telemetry pipeline
- Collect. Run DCGM Exporter on GPU nodes, node_exporter on hosts, kube-state-metrics in Kubernetes, and scheduler exporters or native metrics for Slurm or Kubernetes. Ingest application heartbeats, training throughput, checkpoint progress, network and storage counters, cloud API responses, quota errors, and provider incident or maintenance events.
- Store. Put numeric samples in a time-series database with retention long enough to investigate incidents. Store logs and scheduler events in a searchable event system, and keep job manifests and checkpoint metadata in durable storage.
- Visualize. Create dashboards that follow the same hierarchy: cloud and region, cluster and zone, node, GPU UUID, namespace or team, job, and process. A capacity dashboard should not require a separate tool from a failure dashboard to identify the affected resource.
- Alert. Route alerts by severity and ownership. Include the resource identity, first-seen time, last-seen time, related job IDs, and links to the relevant runbook in every notification.
Labels that make correlation possible
At minimum, preserve labels for cluster, region, zone, node, GPU model, GPU UUID, namespace, pod, team, and workload or job ID. Add Slurm partition and job or step ID where applicable. Keep labels bounded: do not put request IDs, arbitrary user text, full command lines, or every checkpoint name into metric labels. Put high-cardinality details in logs or event records and retain a stable reference in the metric.
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Define two different availability objectives
Service availability answers whether a customer can successfully use the service. Capacity availability answers whether the promised GPU inventory is actually allocatable. Combining them hides important failures: an API can be healthy while no suitable GPU is available, or GPUs can be free while authentication or launch is broken.
| SLI | Customer-visible test | Suggested dimensions | Official NVIDIA interval |
|---|---|---|---|
| Service availability | Authentication plus a small allocation or launch check, with the expected API response and startup result | Offering, region, zone, tenant or plan, and failure class | Measured over a calendar month and tracked every 15 minutes; shorter gaps than the applicable interval are excluded from the SLA calculation |
| Capacity availability | Whether the eligible GPU pool has the requested type, count, and placement available for allocation | GPU type, region, zone, tenancy, partition, and quota | Measured over a calendar month and tracked every 60 minutes; shorter gaps than the applicable interval are excluded from the SLA calculation |
| Internal SLO | Your defined allocation, launch, or placement probe | Explicit eligible pool, interval, maintenance policy, and exclusion rules | Set by your organization |
Document the customer-visible test, eligible capacity pool, measurement interval, and exclusions before publishing an SLO. Record incident start and end, affected offering, attempted use, affected users, and the raw probe result. This lets you reconstruct whether an outage was an API failure, a quota response, a placement failure, or a genuine shortage.
Do not call a GPU that is powered on “available.” A usable capacity calculation should distinguish at least allocatable, reserved, occupied, free, and unusable. A reserved GPU may be idle but unavailable to another tenant; a free GPU may be unusable because its node is not ready or its driver is unhealthy.
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DCGM is designed for NVIDIA GPU health and performance telemetry and can integrate with cluster-management and scheduling systems. Collect the following for each GPU UUID and retain the node and workload identity alongside every sample.
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| Signal | What it reveals | Useful interpretation |
|---|---|---|
| SM or compute utilization | Whether kernels are using the device | Low utilization can mean input starvation, synchronization, an idle allocation, or a broken process; it is not proof of excess capacity. |
| Memory used and free | Allocation pressure and headroom | High memory with low compute can indicate a memory-bound workload, fragmentation, or a process that failed to release allocations. |
| Temperature and power | Thermal or power behavior | Correlate sustained excursions with throttling, clock changes, and node airflow or power events. |
| ECC errors and XID events | Correctable or uncorrectable memory faults and driver-reported GPU failures | Trend by GPU UUID. Repeated or escalating events justify quarantine according to a documented threshold. |
| NVLink and interconnect errors | Peer-to-peer communication health | Distributed jobs can fail or slow even when local utilization looks normal. |
| Metric scrape health | Whether telemetry itself is fresh | A missing sample is an observability incident until you establish that the GPU is healthy. |
Alert on both absolute conditions and changes from a device’s baseline. A single high temperature reading may be normal during a short kernel burst; a repeated thermal throttle, rising XID count, or a scrape gap that coincides with a job stall is stronger evidence. Keep the raw event timestamp, driver version, firmware version, and GPU UUID so a replacement or recurrence can be identified.
Add node, Kubernetes, scheduler, and provider context
Node and Kubernetes layer
Collect node readiness, taints, cordon and drain state, driver and runtime health, filesystem fullness, filesystem latency, network errors, packet drops, link state, and kernel or system logs. From Kubernetes, collect pod phase, restart count, eviction reason, requested versus allocatable GPUs, node placement, device-plugin health, and time-series gaps. A pod stuck pending because of an affinity rule is a placement problem, not a GPU hardware failure.
Scheduler layer
For Slurm or Kubernetes, capture queue depth, pending reason, pending age, requested resources, admission failures, placement failures, start latency, job state, exit status, retries, preemption, and measured utilization. Slurm’s 25.11 metrics guide introduced comprehensive metrics for resources, job states, and scheduler performance. Preserve partition, account, quality-of-service, job, and step identifiers so a queue alert can be tied to the exact policy decision.
Provider layer
Ingest maintenance events, capacity errors, quota responses, API failures, and incident notifications. NVIDIA’s NVSentinel design combines GPU health, system logs, and cloud-provider maintenance events; that combination matters because a planned host action can explain simultaneous failures across otherwise healthy GPUs.
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Measure capacity, queueing, and future demand
Capacity monitoring must show what can be allocated now, what is promised to existing work, and what demand is likely to arrive. NVIDIA’s AI-cloud requirements call for utilization to be monitored and forecast against available capacity, with telemetry detailed to the filesystem, volume, client, Kubernetes pod, and Slurm job ID where applicable.
Inventory and utilization metrics
| Metric group | Measure | Decision it supports |
|---|---|---|
| Inventory | Installed, allocatable, reserved, occupied, free, drained, and failed GPUs by model, region, zone, and tenancy | Whether a request can be admitted and where spare capacity exists |
| Demand | Queue depth, pending age percentiles, requested GPU count, admission failures, quota exhaustion, and placement failures | Whether users are waiting because of shortage, policy, quota, or topology |
| Utilization | GPU utilization p50 and p95, memory pressure, fragmentation, and idle allocation time | Whether capacity is busy, stranded, or inefficiently partitioned |
| Forecast | Demand trend, committed capacity, procurement lead time, expected releases, and safety headroom | When to resize, rebalance regions, or change admission policy |
| Impact | Minutes of capacity shortfall that caused a real customer impact | Whether a shortage breached an SLO rather than merely reducing internal headroom |
Report utilization as a distribution, not only an average. A cluster average of 60% can conceal a saturated partition and a mostly idle partition. Track allocation age and idle allocation time to find jobs that reserve GPUs but do not make progress. Track fragmentation by GPU type and topology: enough total GPUs may exist while no contiguous set satisfies a multi-GPU request.
Forecasting method
- Start with historical demand by GPU type, region, zone, tenant, and time of day.
- Separate committed reservations from opportunistic or cancellable demand.
- Subtract maintenance, failed, drained, and quota-ineligible inventory from installed capacity.
- Project demand and compare it with usable capacity plus an explicit headroom target.
- Record forecast error and revise the model when launch latency, procurement lead time, or workload mix changes.
Expose the forecast beside the current queue and capacity counts. A forecast without the current exclusion set can recommend capacity that will not be usable; a queue without demand trend cannot show whether the problem is transient or structural.
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Create a durable record for every job
Before launch, write a job record that survives a scheduler restart and an application crash. Include job ID, owner or team, code and container version, requested GPU type and count, placement constraints, partition or namespace, checkpoint location, expected heartbeat interval, and the command or workflow version. Add the driver, CUDA, and framework versions when they are known.
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During execution, record scheduler state transitions, allocation and start timestamps, throughput, heartbeat time, checkpoint start and completion, retry count, preemption, exit code, signal, pod events, and the GPU UUIDs used. Store application logs, system logs, DCGM events, network counters, storage latency and errors, and provider events with synchronized timestamps.
Failure attribution sequence
- Detect. Trigger on a missing heartbeat, stalled throughput, abnormal exit, scheduler failure, or GPU or system event.
- Enrich. Attach cluster, zone, node, GPU UUID, pod or step, job, driver, image, checkpoint, and recent maintenance data.
- Classify. Assign a provisional class: application, scheduler, capacity, node, GPU, network, storage, or provider incident. Keep the evidence and confidence with the classification.
- Contain. If the evidence crosses a defined threshold, quarantine or cordon the suspect resource and drain it safely. Do not remove a node solely because one job reported an application exception.
- Recover. Retry with a bounded attempt count, changed placement, or a checkpoint resume. Require an explicit policy for when a retry is unsafe.
- Learn. Record recovery duration, lost GPU-hours, checkpoint age at failure, and whether the same node or GPU recurs.
Evidence patterns by fault domain
| Likely domain | Corroborating evidence | First response |
|---|---|---|
| Application or software | Repeatable exception on different healthy nodes, identical image and code, no matching hardware or network event | Stop automatic retries, preserve logs, and return the error to the owner or release pipeline |
| GPU | XID or ECC events, NVLink errors, abnormal temperature or power, and recurrence tied to one GPU UUID | Quarantine the GPU or node according to policy, then run hardware diagnostics |
| Node or driver | Node-not-ready transition, runtime or device-plugin errors, kernel faults, or failures affecting multiple GPUs on one host | Cordon, drain, collect diagnostics, and repair or replace the host |
| Network | Packet loss, link errors, collective-operation timeout, or correlated failures across nodes or racks | Check fabric and interfaces; avoid retrying onto the same failed path |
| Storage | Checkpoint write latency, I/O errors, filesystem saturation, or failures shared by jobs using one volume | Protect checkpoint integrity, move to a healthy volume, and investigate the storage path |
| Scheduler or capacity | Pending reason, quota exhaustion, placement failure, preemption, or no suitable topology despite free aggregate GPUs | Correct policy or capacity; do not label the event as a hardware fault |
| Provider incident | API errors, maintenance notification, regional impact, or simultaneous host events | Apply the provider runbook and communicate the affected offering and time window |
Checkpoint awareness is essential. A restart that discards hours of progress can cost more than the original failure. NVIDIA’s Autonomous Job Recovery design describes monitoring checkpoints, attributing failures across compute, network, storage, node, GPU, and software signals, then isolating and restarting or resuming work.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Detect a bad GPU before it consumes another job
Use a combination of persistent identity, health events, and workload symptoms rather than a single threshold.
- Detect an error, scrape gap, thermal throttle, unexplained utilization collapse, or job stall.
- Map the event to the GPU UUID, node, driver, firmware, pod, and job timeline.
- Check whether the same GPU has repeated correctable errors, XID events, interconnect errors, or failed diagnostics.
- Compare neighboring GPUs in the same node and the device’s recent baseline. A cluster-wide change suggests a shared node, fabric, or software cause.
- Apply a documented quarantine threshold. Cordon the node or remove the device from scheduling only when evidence is sufficient, and attach the event IDs to the action.
- Drain workloads safely, preserve logs and diagnostics, and release the resource only after validation. Record whether the fault follows the GPU, the node, or the workload.
Never silently recycle a suspect device. A restart may clear a symptom while allowing the same GPU to fail a second job. Quarantine, action owner, start time, end time, diagnostic result, and release decision should all be auditable.
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Design alerts and automated recovery with guardrails
Use at least three severity levels, as required in NVIDIA’s AI-cloud requirements, and track acknowledgement. NVIDIA also specifies telemetry-ingestion latency of no more than 120 seconds, 24x7x365 on-call coverage, acknowledgement tracking, and retention aligned to investigation needs.
| Severity | Example condition | Action |
|---|---|---|
| Critical | Customer launch probe fails, a regional capacity pool is unavailable, or multiple jobs fail with a correlated provider or fabric event | Page the on-call engineer, open an incident, start the customer-impact timeline, and protect checkpoints |
| High | Repeated XID or ECC errors on one GPU, node-not-ready with running work, or a checkpoint heartbeat missing beyond policy | Page the owning team, quarantine or drain when the runbook threshold is met, and begin recovery |
| Warning | Pending-age percentile rising, forecast headroom shrinking, scrape gaps, or sustained memory pressure | Create a ticket or notify capacity operations before customers are affected |
Bound retries by count, elapsed time, and placement diversity. A safe policy might require a new node or zone after a hardware-class failure and stop after a small number of attempts; the exact limits belong in your runbook. Every automated cordon, drain, restart, or resume should include the triggering evidence, policy version, actor, and rollback path.
Implement the system in practical stages
- Inventory the control points. List GPU models and UUIDs, clusters, zones, partitions, namespaces, device plugins, schedulers, storage systems, network fabrics, cloud APIs, and maintenance feeds.
- Standardize identity. Define stable labels and a job ID that appears in scheduler records, pod labels, application logs, checkpoints, and alerts.
- Install collectors. Deploy DCGM Exporter, node_exporter, kube-state-metrics, scheduler metrics, log collection, and provider-event ingestion. Measure scrape freshness and ingestion delay from the start.
- Build four dashboards. Create GPU health, cluster readiness, capacity and queueing, and job-investigation views with shared filters for region, node, GPU UUID, team, and job.
- Write the SLI definitions. Publish service and capacity probes, eligible pools, intervals, exclusions, and incident accounting.
- Instrument jobs. Emit heartbeat, throughput, checkpoint, exit, and retry events. Save the durable pre-launch record before allocating GPUs.
- Write runbooks. Define evidence thresholds for application, GPU, node, network, storage, scheduler, and provider faults, including quarantine and rollback procedures.
- Test failure paths. Exercise a missing heartbeat, failed checkpoint, node drain, quota rejection, placement failure, scrape outage, and provider maintenance event. Verify that alerts identify the right owner and that recovery does not overwrite the last valid checkpoint.
- Review weekly and after incidents. Examine false alerts, lost GPU-hours, repeated GPU UUIDs, queue-age trends, forecast error, acknowledgement time, and recovery success.
Compare monitoring tools and GPU-cloud providers on operations, not screenshots
When evaluating an implementation, score the following capabilities explicitly:
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute- Signal coverage: GPU, node, Kubernetes, Slurm, application, network, storage, quota, and provider-maintenance events.
- Attribution: Resolution from alert to cluster, node, GPU UUID, pod, namespace, job, team, and checkpoint.
- Freshness and loss: Scrape latency, sample gaps, event-delivery delay, backfill behavior, and retention.
- Capacity semantics: Separate allocatable, reserved, occupied, free, failed, and quota-ineligible inventory; include forecasting and topology-aware placement.
- Scheduler integration: Pending reasons, pending age, admission and placement failures, preemption, retries, start latency, and exit data.
- Recovery: Checkpoint awareness, safe retry, changed placement, resource quarantine, audit trail, and rollback.
- Operations: 24×7 response, acknowledgement tracking, severity policy, runbooks, and investigation retention.
Ask vendors or internal platform teams to demonstrate a complete incident, not just a dashboard: start with a failed launch or job heartbeat, identify the affected GPU and job, show the provider event, quarantine the resource, resume from a checkpoint, and produce an audit record. If any handoff requires a manual spreadsheet or an unlabelled log search, the apparent observability coverage is incomplete.
Common monitoring mistakes and corrections
- Watching only utilization. Add health events, memory, temperature, power, interconnect, scrape status, queueing, and application progress.
- Counting powered-on GPUs as capacity. Subtract reservations, failed and drained devices, quota restrictions, maintenance, and topology constraints.
- Using one availability number. Publish separate service and capacity SLIs with explicit tests and exclusions.
- Alerting on every transient event. Correlate duration, recurrence, workload impact, and neighboring-device behavior before paging.
- Retrying without changing evidence or placement. Bound retries, preserve the checkpoint, and move away from a suspect node, GPU, path, or zone.
- Losing identity in high-cardinality labels. Keep stable bounded labels in metrics and put detailed request and checkpoint data in logs and job records.
- Ignoring observability outages. Alert on stale scrapes, missing exporters, delayed ingestion, and broken event delivery; unknown health is not healthy.
- Automating destructive actions without audit. Require thresholds, approvals where appropriate, policy versions, action logs, and rollback procedures for cordon, drain, restart, and quarantine.
A reliable GPU cloud is therefore measured as a chain: can a customer authenticate and launch, is the requested capacity genuinely usable, does the job make progress, and can a failure be attributed and recovered without repeating the loss? The monitoring stack is successful when it answers those questions with current evidence and an accountable action, not when it merely displays more GPU gauges.
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