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Use NVIDIA DCGM Exporter to expose GPU telemetry, configure Prometheus to scrape its /metrics endpoint, then connect Grafana to Prometheus to graph the resulting series. The exporter listens on port 9400 by default. For a fleet, run an exporter on each GPU node and verify both the Prometheus target and an expected DCGM metric before building dashboards.
How the monitoring pipeline works
NVIDIA Data Center GPU Manager (DCGM) provides libraries and tools for observing, managing, validating, and integrating NVIDIA data-center accelerators. DCGM Exporter uses DCGM through libdcgm and converts selected telemetry fields into Prometheus exposition format. It normally initializes an embedded host engine, but can connect to a separately managed host engine using a supported transport. See NVIDIA’s DCGM documentation and DCGM Exporter installation guide.
- Collect: DCGM Exporter reads the configured fields from GPUs on its node.
- Scrape: Prometheus reaches the exporter’s HTTP endpoint and stores the returned samples.
- Visualize: Grafana queries Prometheus and displays the time series.
Starting an exporter does not configure Prometheus or Grafana. Each part of the pipeline needs to be deployed and connected.
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For ordinary coverage of a GPU fleet, deploy one exporter per GPU node. The appropriate route depends on whether the machines are standalone Linux hosts or Kubernetes workers, and whether your organization already manages GPU software through the NVIDIA GPU Operator.
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| Environment | Deployment route | What to account for |
|---|---|---|
| Linux host | Package-managed executable and systemd service | Uses the host’s service-management and package-update workflow. |
| Linux host or standalone container | OCI container | Container deployment needs NVIDIA GPU runtime access and the NVIDIA Container Toolkit. |
| Kubernetes | DCGM Exporter Helm chart | Deploys exporter pods on selected GPU nodes and supports Kubernetes pod mapping. |
| Kubernetes using NVIDIA GPU Operator | GPU Operator-managed stack | Can manage drivers, container toolkit, DCGM, exporter, and related Kubernetes resources together. |
Follow NVIDIA’s installation guide for the documented host or container method that fits your environment. Before rollout, check NVIDIA’s product support information for the GPU, driver, and DCGM combination you run; compatibility is not universal across hardware and software versions.
Deploy on Kubernetes
NVIDIA’s DCGM Exporter Helm chart is a direct option when you want to manage the exporter without adopting the GPU Operator for the wider GPU software stack. NVIDIA also documents deployment through the GPU Operator.
Helm chart and ServiceMonitor discovery
The chart enables a ServiceMonitor by default when Prometheus Operator custom resource definitions (CRDs) are present. If those CRDs are not installed, disable the ServiceMonitor or configure another discovery method. For Prometheus Operator to use the chart-created ServiceMonitor, the Prometheus resource’s ServiceMonitor and namespace selectors must admit both the ServiceMonitor and its namespace. If your Prometheus instance selects ServiceMonitors by label, set the additional labels required by that selector in the chart configuration. The chart’s discovery requirements are described in NVIDIA’s Prometheus configuration guide.
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GPU Operator ownership
Choose the GPU Operator when it already owns, or is intended to own, the cluster’s GPU software components. If the cluster uses a separately managed driver and runtime stack, a direct Helm deployment may fit more naturally. Avoid managing the same components through competing mechanisms.
Configure Prometheus to scrape the exporter
DCGM Exporter serves metrics at /metrics on port 9400 by default. Prometheus must be able to reach that endpoint from its own network. For a static-target setup, NVIDIA documents a dcgm-exporter job with a 30-second scrape interval, a 25-second timeout, and each GPU node as a port-9400 target. These are example values, not universal requirements; use service discovery appropriate to your environment and tune the timing to the exporter’s response behavior.
A static-target configuration follows this pattern; replace the example address with a reachable GPU node address:
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scrape_configs:
- job_name: dcgm-exporter
scrape_interval: 30s
scrape_timeout: 25s
static_configs:
- targets: ['gpu-node.example:9400']For Kubernetes with Prometheus Operator, the chart’s ServiceMonitor can provide discovery when the CRDs and selectors are configured as described above. If you use static configuration or another discovery mechanism instead, ensure targets track the GPU nodes you intend to monitor.
Keep collection and scrape timing distinct
There are several independent timing controls: DCGM’s collection interval, any YAML watch-group intervals, Prometheus’s scrape_interval and scrape_timeout, and the exporter’s HTTP write timeout. Scraping more frequently than DCGM refreshes a field can return the same cached sample; a Prometheus scrape does not itself force a new hardware query. NVIDIA advises keeping scrape_timeout below both scrape_interval and the exporter’s --web-write-timeout. Its command reference shows an example YAML configuration with a 30-second collection interval and a separate 5-second fast-thermals watch group for temperature and board-power fields; treat those as example settings, not general defaults or recommendations. See the DCGM Exporter command-line reference and Prometheus configuration guide.
Account for network security
The default endpoint and NVIDIA’s chart ServiceMonitor guidance assume unauthenticated HTTP. Do not expose that setup to an untrusted network without appropriate controls. DCGM Exporter supports Prometheus exporter-toolkit web configuration for TLS or basic authentication. If you enable either, the default ServiceMonitor will not automatically provide matching credentials or TLS settings: disable it and supply scrape configuration that matches the exporter’s security configuration.
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Verify collection and scraping
Check the path from the Prometheus network to the metric before troubleshooting dashboards. For a host deployment, the local check below confirms that the exporter responds on its default endpoint:
curl --fail http://localhost:9400/metricsA successful response includes metric names beginning with DCGM_. For a remote Prometheus or Kubernetes deployment, test reachability from the Prometheus network; in Kubernetes, you can port-forward the exporter Service to inspect its endpoint locally.
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dcgm-exportertarget isUP. - Run
up{job="dcgm-exporter"}in Prometheus to confirm that the scrape is succeeding. - Run
DCGM_FI_DEV_GPU_UTILto check whether that selected GPU-utilization series is present.
If the target is down, investigate endpoint reachability, target discovery, service routing, and whether Prometheus’s selectors admit the ServiceMonitor. If the target is up but a particular metric is absent, check the selected fields and whether that field is available for the GPU, DCGM, driver, and runtime configuration in use.
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Select and interpret GPU metrics
The exporter’s collector CSV, ConfigMap, or YAML configuration determines which DCGM fields it exposes. NVIDIA’s DCGM Exporter metrics reference lists shipped defaults including SM and memory clocks, GPU and memory temperatures, board power usage, total energy consumption, and PCIe TX bytes. It also documents optional exporter-owned metric families for clock events, XID errors, GPU health, and P2P status.
Use the DCGM field reference to understand a field’s meaning and units. Some Prometheus metric names are legacy aliases for canonical DCGM field identifiers. A field appearing in a configuration does not guarantee it will be emitted: availability depends on DCGM, GPU or entity type, driver, permissions, and deployment settings. The endpoint output on the target system is the definitive check. The exporter uses a configured row’s Prometheus type as written; it does not infer or semantically validate that type against the DCGM field definition.
- Use shipped defaults when they cover the operational questions you need to answer.
- Customize the field set when you need additional signals, but verify each field on the actual fleet and account for metric cardinality and collection overhead.
- Set collection cadence deliberately: faster refreshes can make telemetry fresher but should be balanced against collection behavior and the required signal.
Connect Grafana and build a view
In Grafana, configure Prometheus as a data source, then create a panel that queries a metric returned by the exporter, such as DCGM_FI_DEV_GPU_UTIL. Confirm that the query returns series before building dashboards around it. NVIDIA’s version 3.1 GPU telemetry guide describes the stack and points to an NVIDIA dashboard; because that dashboard reference comes from version 3.1 documentation, check the current listing and compatibility before importing it: NVIDIA DCGM-Exporter GPU telemetry guide.
Quick Recap
Troubleshoot missing data
The exporter endpoint does not respond
- Confirm the exporter is running on the intended GPU node and listening on port
9400, unless you changed its configuration. - For a container deployment, confirm the NVIDIA Container Toolkit and GPU runtime access are configured.
- Check network paths and service routing between Prometheus and the exporter.
The Prometheus target is down
- Check the target address, port, and path, along with routing and firewall rules.
- For Kubernetes ServiceMonitor discovery, verify the Prometheus resource’s namespace and label selectors, the ServiceMonitor labels, and the presence of the Prometheus Operator CRDs.
- If TLS or basic authentication is enabled at the exporter, configure matching scrape credentials and TLS settings rather than relying on the default unauthenticated ServiceMonitor.
The target is up but a metric is missing
- Inspect the endpoint output to see which
DCGM_metrics are actually emitted. - Confirm the field is selected in the collector configuration and supported by the specific GPU, entity type, DCGM, driver, and runtime permissions.
- Check collection and scrape cadence: scraping faster than DCGM refreshes data may repeat cached samples, and does not force a new hardware query.
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