Nvidia GPU

Plugin: go.d.plugin Module: nvidia_smi

Overview

This collector monitors GPUs performance metrics using the nvidia-smi CLI tool.

This collector is supported on all platforms.

This collector supports collecting metrics from multiple instances of this integration.

Nvidia GPU can be monitored further using the following other integrations:

• Nvidia Data Center GPU Manager (DCGM)

Default Behavior

Auto-Detection

This integration doesn't support auto-detection.

Limits

The default configuration for this integration does not impose any limits on data collection.

Performance Impact

The default configuration for this integration is not expected to impose a significant performance impact on the system.

Setup

You can configure the nvidia_smi collector in two ways:

Method	Best for	How to
UI	Fast setup without editing files	Go to Nodes → Configure this node → Collectors → Jobs, search for nvidia_smi, then click + to add a job.
File	If you prefer configuring via file, or need to automate deployments (e.g., with Ansible)	Edit go.d/nvidia_smi.conf and add a job.

important

UI configuration requires paid Netdata Cloud plan.

Prerequisites

No action required.

Configuration

Options

The following options can be defined globally: update_every, autodetection_retry.

Config options

Option	Description	Default	Required
update_every	Data collection frequency.	10	no
autodetection_retry	Autodetection retry interval (seconds). Set 0 to disable.	0	no
binary_path	Path to the nvidia-smi binary.	nvidia-smi	no
timeout	Command execution timeout, in seconds. In loop mode, it limits the wait for each nvidia-smi sample instead.	10	no
loop_mode	Keep nvidia-smi running and reading GPU data on a fixed interval (the -l option) instead of starting it for every collection.	yes	no

binary_path

When it is empty or the file does not exist, the collector looks for nvidia-smi in the PATH directories and, on Windows, in the default NVIDIA install locations.

timeout

In loop mode, the collector waits up to this timeout for the first sample after it starts. Afterwards, a sample that does not arrive within the loop interval plus this timeout leaves a gap in the charts, and nvidia-smi is restarted. On hosts with several GPUs, the first sample can take 5 to 10 seconds.

loop_mode

The loop interval is the data collection interval (update_every), at most 5 seconds. Loop mode is not available on Windows: there, each collection runs nvidia-smi once.

via UI

Configure the nvidia_smi collector from the Netdata web interface:

1. Go to Nodes.
2. Select the node where you want the nvidia_smi data-collection job to run and click the ⚙ (Configure this node). That node will run the data collection.
3. The Collectors → Jobs view opens by default.
4. In the Search box, type nvidia_smi (or scroll the list) to locate the nvidia_smi collector.
5. Click the + next to the nvidia_smi collector to add a new job.
6. Fill in the job fields, then click Test to verify the configuration and Submit to save.
   • Test validates the provided settings and checks the collector's startup prerequisites. Successful validation does not guarantee that every metric will be available during collection.
   • If it fails, an error message appears with details (for example, connection refused, timeout, or command execution errors), so you can adjust and retest.

via File

The configuration file name for this integration is go.d/nvidia_smi.conf.

The file format is YAML. Generally, the structure is:

update_every: 1
autodetection_retry: 0
jobs:
  - name: some_name1
  - name: some_name2

You can edit the configuration file using the edit-config script from the Netdata config directory.

cd /etc/netdata 2>/dev/null || cd /opt/netdata/etc/netdata
sudo ./edit-config go.d/nvidia_smi.conf

Examples

Custom binary path

The executable is not in the directories specified in the PATH environment variable.

Config

jobs:
  - name: nvidia_smi
    binary_path: /usr/local/sbin/nvidia_smi

Alerts

There are no alerts configured by default for this integration.

Metrics

Metrics grouped by scope.

The scope defines the instance that the metric belongs to. An instance is uniquely identified by a set of labels.

Per gpu

These metrics refer to the GPU.

Labels:

Label	Description
uuid	GPU uuid (e.g. GPU-27b94a00-ed54-5c24-b1fd-1054085de32a)
index	GPU index (nvidia_smi typically orders GPUs by PCI bus ID)
product_name	GPU product name (e.g. NVIDIA A100-SXM4-40GB)

Metrics:

Metric	Description	Dimensions	Unit
nvidia_smi.gpu_pcie_bandwidth_usage	PCI Express Bandwidth Usage	rx, tx	B/s
nvidia_smi.gpu_pcie_bandwidth_utilization	PCI Express Bandwidth Utilization	rx, tx	%
nvidia_smi.gpu_fan_speed_perc	Fan speed	fan_speed	%
nvidia_smi.gpu_utilization	GPU utilization	gpu	%
nvidia_smi.gpu_memory_utilization	Memory utilization	memory	%
nvidia_smi.gpu_decoder_utilization	Decoder utilization	decoder	%
nvidia_smi.gpu_encoder_utilization	Encoder utilization	encoder	%
nvidia_smi.gpu_frame_buffer_memory_usage	Frame buffer memory usage	free, used, reserved	B
nvidia_smi.gpu_bar1_memory_usage	BAR1 memory usage	free, used	B
nvidia_smi.gpu_temperature	Temperature	temperature	Celsius
nvidia_smi.gpu_voltage	Voltage	voltage	V
nvidia_smi.gpu_clock_freq	Clock current frequency	graphics, video, sm, mem	MHz
nvidia_smi.gpu_power_draw	Power draw	power_draw	Watts
nvidia_smi.gpu_performance_state	Performance state	P0-P15	state
nvidia_smi.gpu_mig_mode_current_status	MIG current mode	enabled, disabled	status
nvidia_smi.gpu_mig_devices_count	MIG devices	mig	devices

Per mig

These metrics refer to the Multi-Instance GPU (MIG).

Labels:

Label	Description
uuid	GPU uuid (e.g. GPU-27b94a00-ed54-5c24-b1fd-1054085de32a)
product_name	GPU product name (e.g. NVIDIA A100-SXM4-40GB)
gpu_instance_id	GPU instance id (e.g. 1)

Metrics:

Metric	Description	Dimensions	Unit
nvidia_smi.gpu_mig_frame_buffer_memory_usage	Frame buffer memory usage	free, used, reserved	B
nvidia_smi.gpu_mig_bar1_memory_usage	BAR1 memory usage	free, used	B

Troubleshooting

Diagnostics

Debug Mode

Important: Debug mode is not supported for data collection jobs created via the UI using the Dyncfg feature.

To troubleshoot issues with the nvidia_smi collector, run the go.d.plugin with the debug option enabled. The output should give you clues as to why the collector isn't working.

• Navigate to the plugins.d directory, usually at /usr/libexec/netdata/plugins.d/. If that's not the case on your system, open netdata.conf and look for the plugins setting under [directories].

  cd /usr/libexec/netdata/plugins.d/

• Switch to the netdata user.

  sudo -u netdata -s

• Run the go.d.plugin to debug the collector:

  ./go.d.plugin -d -m nvidia_smi

  To debug a specific job:

  ./go.d.plugin -d -m nvidia_smi -j jobName

Getting Logs

If you're encountering problems with the nvidia_smi collector, follow these steps to retrieve logs and identify potential issues:

• Run the command specific to your system (systemd, non-systemd, or Docker container).
• Examine the output for any warnings or error messages that might indicate issues. These messages should provide clues about the root cause of the problem.

System with systemd

Use the following command to view logs generated since the last Netdata service restart:

journalctl _SYSTEMD_INVOCATION_ID="$(systemctl show --value --property=InvocationID netdata)" --namespace=netdata --grep nvidia_smi

System without systemd

Locate the collector log file, typically at /var/log/netdata/collector.log, and use grep to filter for collector's name:

grep nvidia_smi /var/log/netdata/collector.log

Note: This method shows logs from all restarts. Focus on the latest entries for troubleshooting current issues.

Docker Container

If your Netdata runs in a Docker container named "netdata" (replace if different), use this command:

docker logs netdata 2>&1 | grep nvidia_smi