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Use NVIDIA DCGM Exporter to expose selected GPU telemetry at /metrics, have Prometheus scrape that endpoint, then query the collected series in Grafana. The exporter normally listens on port 9400. You need one exporter instance per GPU node, network access from Prometheus to each endpoint, and a compatible GPU, driver, and DCGM combination.
How the monitoring pipeline works
NVIDIA Data Center GPU Manager (DCGM) provides tools and libraries for observing and managing NVIDIA data-center accelerators. DCGM Exporter uses DCGM to convert selected telemetry fields into Prometheus exposition format; NVIDIA describes it as: “DCGM Exporter converts selected DCGM telemetry fields to the Prometheus exposition format.” NVIDIA’s installation guide and DCGM documentation cover the components and supported deployment paths.
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- Collect: DCGM Exporter obtains the configured fields from the GPU node.
- Expose: The exporter serves those fields at
/metrics, on port9400by default. - Scrape: Prometheus requests the endpoint at its configured interval and stores the resulting time series.
- Visualize: Grafana queries Prometheus to display charts and dashboards.
Running an exporter does not configure Prometheus automatically. A successful setup requires both a reachable endpoint and a Prometheus scrape configuration or service-discovery mechanism.
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Deploy one exporter on each GPU node you intend to monitor. Select a deployment method that fits how the host or cluster already manages software and GPU access.
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| Environment | Deployment route | Best fit and key dependency |
|---|---|---|
| Linux host | Package-managed executable and systemd service | Useful when host services and updates are managed through the operating system’s package and service tools. |
| Linux host | Standalone OCI container | Useful when workloads are containerized; the container needs NVIDIA GPU runtime access and the NVIDIA Container Toolkit. |
| Kubernetes | DCGM Exporter Helm chart, typically a DaemonSet | Places exporter pods on selected GPU nodes. ServiceMonitor discovery requires Prometheus Operator CRDs and matching selectors and labels. |
| Kubernetes | NVIDIA GPU Operator | Fits clusters already using the Operator to manage GPU drivers, container toolkit, DCGM, exporter, and related resources as a stack. |
Before rollout, check NVIDIA’s product support information for the GPU, driver, and DCGM combination in use. Deployment requirements and supported fields vary by hardware and software compatibility. NVIDIA documents the exporter installation options and the command-line configuration.
Validate a host or container deployment
After starting the exporter, test the local endpoint on the GPU node:
curl --fail http://localhost:9400/metrics
A working response should include metric names beginning with DCGM_. If Prometheus runs on another host or cluster, also confirm that its network can reach the exporter’s HTTP endpoint; a localhost test alone does not prove remote reachability.
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Deploy in Kubernetes
NVIDIA’s Helm-chart guide describes a deployment that places exporter pods on GPU nodes and enables Kubernetes pod mapping. The chart enables its ServiceMonitor by default when Prometheus Operator CRDs are available. If those CRDs are absent, disable ServiceMonitor until they are installed or use another discovery configuration.
For ServiceMonitor discovery to work, the Prometheus resource must select the ServiceMonitor and its namespace. If it filters ServiceMonitors by label, set the additional labels required by the chart so the Prometheus selectors admit it. The NVIDIA installation guide explains the chart options; the Prometheus configuration guide covers scraping and discovery.
Configure Prometheus to scrape GPU metrics
NVIDIA’s static-target example uses a job named dcgm-exporter, a 30s scrape interval, a 25s scrape timeout, and GPU-node targets on port 9400. These are example values, not universal requirements. Adapt the target discovery method and timeouts to the number of nodes, network conditions, and exporter response behavior. For static discovery, a minimal job has this shape:
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scrape_configs:
- job_name: dcgm-exporter
scrape_interval: 30s
scrape_timeout: 25s
static_configs:
- targets:
- gpu-node-1.example:9400
- gpu-node-2.example:9400
Replace the example hostnames with addresses Prometheus can resolve and reach. For Kubernetes, use the ServiceMonitor or another discovery method that matches the cluster’s Prometheus setup rather than copying a static-host example unchanged.
Keep collection and scrape timing distinct
There are several independent timing controls: DCGM’s collection interval, any YAML watch-group interval, Prometheus’s scrape interval and timeout, and the exporter’s HTTP write timeout. Scraping faster than DCGM’s effective collection cadence can return the same cached sample more than once; 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. Shorter intervals can make data available to queries sooner, but they also increase scrape and storage activity; tune them against the collection cadence and response budget. The Prometheus configuration reference describes these settings.
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Match authentication and TLS settings
The chart’s default ServiceMonitor assumes unauthenticated HTTP. If the exporter uses TLS or basic authentication, disable that ServiceMonitor and configure Prometheus scraping with the matching credentials and TLS settings. DCGM Exporter supports Prometheus exporter-toolkit web configuration for TLS or basic authentication. Do not expose an unauthenticated endpoint to an untrusted network; restrict access or configure appropriate transport security and credentials.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Verify the endpoint, target, and metric
Check each layer in order so a missing graph can be traced to collection, reachability, scraping, or querying.
- From the Prometheus network, request the exporter’s
/metricsendpoint and confirm it returns metrics. For a Kubernetes service, port-forward it or test it from a network location with equivalent access. - Open Prometheus’s Targets page and confirm the
dcgm-exportertarget isUP. - In Prometheus’s query interface, run
up{job="dcgm-exporter"}. A value of1indicates that Prometheus considers the target up. - Query
DCGM_FI_DEV_GPU_UTILto check whether GPU utilization telemetry is actually being emitted and scraped. - In Grafana, add or select the Prometheus data source and graph the collected metric series.
NVIDIA’s version 3.1 GPU telemetry guide also points to an NVIDIA dashboard. Because that reference is version-specific, check the dashboard listing and its compatibility before importing it.
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Choose which GPU fields to export
The exporter’s collector CSV, ConfigMap, or YAML configuration determines which DCGM fields it exposes. NVIDIA’s metrics reference lists shipped defaults such as SM and memory clocks, GPU and memory temperatures, board power usage, total energy consumption, and PCIe transmit bytes. It also documents optional exporter-owned metric families for clock events, XID errors, GPU health, and peer-to-peer status.
Some metric names are legacy aliases for canonical DCGM field identifiers. Use the DCGM field reference for a field’s meaning and units rather than inferring them from its name. A configured field is not guaranteed to appear at runtime: availability can depend on DCGM, entity type, GPU model, driver, permissions, and deployment configuration. The endpoint output on the target system is the definitive check.
Defaults or a custom collector?
- Start with shipped defaults when you need common telemetry and want a simple initial setup.
- Use a custom collector configuration when you need specific fields or want to avoid collecting signals your monitoring and alerting do not use.
- Check cardinality and support before adding fields across a fleet. Field selection affects the series Prometheus stores, and a field that works on one GPU or driver combination may not be available on another.
NVIDIA’s command reference shows an example YAML configuration with a 30s collection interval and a separate fast-thermals watch group at 5s for temperature and board-power fields. These are example settings, not the exporter’s universal defaults or a recommendation for all hardware. See the command reference and metrics reference when selecting fields and intervals.
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Troubleshoot a down target or missing series
| Symptom | What to check |
|---|---|
Local /metrics request fails |
Confirm the exporter is running, listening on the expected interface and port, and able to access the GPU through the host driver or NVIDIA container runtime. |
| Local request works, but Prometheus target is down | Check DNS, routing, firewall rules, service ports, and whether Prometheus can reach the exporter from its own network. Verify that the configured target address and port are correct. |
| Kubernetes exporter pod exists, but no target is discovered | Check that Prometheus Operator CRDs are installed, the ServiceMonitor is enabled, and Prometheus’s namespace and label selectors include the ServiceMonitor. Confirm chart labels match any required Prometheus selector labels. |
| Target is down after enabling TLS or basic authentication | Ensure the scrape configuration uses the same authentication and TLS settings as the exporter. The chart’s default ServiceMonitor assumes unauthenticated HTTP. |
| Target is up, but a metric is absent | Check the collector configuration, the exact field name and units in NVIDIA’s metrics reference, and whether the GPU, DCGM, driver, entity type, and permissions support it. Inspect the endpoint directly to distinguish a collection issue from a query or dashboard issue. |
| Repeated values appear despite frequent scrapes | Compare Prometheus’s scrape interval with DCGM’s effective collection cadence and any watch-group interval; scraping faster can repeat cached samples instead of triggering new hardware reads. |
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