<!--startmeta
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meta_yaml: "https://github.com/netdata/netdata/edit/master/src/go/plugin/go.d/collector/nvidia_smi/metadata.yaml"
sidebar_label: "Nvidia GPU"
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keywords: ['nvidia', 'gpu', 'hardware']
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# Nvidia GPU


<img src="https://netdata.cloud/img/nvidia.svg" width="150"/>


Plugin: go.d.plugin
Module: nvidia_smi

<img src="https://img.shields.io/badge/maintained%20by-Netdata-%2300ab44" />

## Overview

This collector monitors GPUs performance metrics using
the [nvidia-smi](https://developer.nvidia.com/nvidia-system-management-interface) CLI tool.




This collector is supported on all platforms.

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


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

- [Nvidia Data Center GPU Manager (DCGM)](/src/go/plugin/go.d/collector/dcgm/integrations/nvidia_data_center_gpu_manager_dcgm.md)

### 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**](#via-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**](#via-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.


<details open><summary>Config options</summary>



| 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 nvidia_smi binary. The default is "nvidia_smi" and the executable is looked for in the directories specified in the PATH environment variable. | nvidia_smi | no |
| timeout | The maximum duration, in seconds, to wait for an `nvidia-smi` command to complete. This setting applies differently based on the collector's mode. **Loop Mode:** In loop mode, the timeout primarily determines how long to wait for the initial `nvidia-smi` execution. If the initial query takes longer than the timeout, the collector may report an error. For systems with multiple GPUs, the initial load time can sometimes be significant (e.g., 5-10 seconds). **Regular Mode:** If the collector is in regular mode, the timeout specifies how long to wait for each individual `nvidia-smi` execution. | 10 | no |
| loop_mode | When enabled, `nvidia-smi` is executed continuously in a separate thread using the `-l` option. | yes | no |


</details>


#### 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 :gear: (**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** runs the job with the provided settings and shows whether data can be collected.
    - 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:

```yaml
update_every: 1
autodetection_retry: 0
jobs:
  - name: some_name1
  - name: some_name2
```
You can edit the configuration file using the [`edit-config`](https://github.com/netdata/netdata/blob/master/docs/netdata-agent/configuration/README.md#edit-configuration-files) script from the
Netdata [config directory](https://github.com/netdata/netdata/blob/master/docs/netdata-agent/configuration/README.md#locate-your-config-directory).

```bash
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.

<details open><summary>Config</summary>

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

```
</details>



## 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

### 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]`.

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

- Switch to the `netdata` user.

  ```bash
  sudo -u netdata -s
  ```

- Run the `go.d.plugin` to debug the collector:

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

  To debug a specific job:

  ```bash
  ./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:

```bash
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:

```bash
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:

```bash
docker logs netdata 2>&1 | grep nvidia_smi
```
