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gpu monitor

Name: gpu monitor
Author: tinkerjueberg

作者 TinkerJueBerg · GitHub ↗ · v1.0.0 · MIT-0

cross-platform ⚠ suspicious

总下载

当前安装

版本数

在 OpenClaw 中安装

/install gpu-monitor

功能描述

Provides real-time NVIDIA GPU usage and memory stats, plus Ollama model layer GPU/CPU distribution via server.log parsing with live updates.

使用说明 (SKILL.md)

GPU Monitor - Ollama Real-time GPU Monitoring Skill

Overview

This skill provides real-time GPU monitoring for local Ollama models. It monitors:

GPU name and memory usage with utilization percentage (e.g., 8.5/10.0 GB = 85%)
Model layer distribution (GPU vs CPU offloading) via Ollama server.log parsing
Live status updates every 2 seconds

⚠️ Framework Dependency: This skill is specifically designed for the Ollama framework (https://ollama.ai).
📝 Log Requirement: Requires access to Ollama's server.log file at a configurable path to parse model layer information.

Features

✅ Ollama-specific monitoring: Automatically parses server.log for model info when available
✅ Layer distribution tracking: Shows GPU layers, total layers, and CPU offload percentage
✅ Memory visualization: Displays memory used/total with real-time utilization %
✅ Cross-platform: Works on Windows/Linux/macOS with NVIDIA GPUs via nvidia-smi
✅ Real-time updates: Configurable refresh interval (default: 2 seconds)
✅ Flexible configuration: Specify Ollama log path via CLI --ollama-log=PATH or config file
✅ Graceful degradation: Shows GPU metrics even without Ollama installed

Installation

# Via ClawHub
clawhub install gpu-monitor-skill

# Or manual clone
git clone \x3Crepository-url> ~/.openclaw/skills/gpu-monitor

Usage (Local Testing)

# Basic usage - monitors local GPU
python ~/.openclaw/clawhub/gpu-monitor-skill/gpu_monitor.py --interval=3

# With Ollama log path for layer tracking
python ~/.openclaw/clawhub/gpu-monitor-skill/gpu_monitor.py \
    --ollama-log="C:\Users\zugzwang\AppData\Local\Ollama\server.log" \
    --interval=2

# Using config file (create ~/.openclaw/gpu_monitor_config.json)
{
  "update_interval_seconds": 2,
  "ollama_log_path": "/path/to/server.log",
  "quiet_mode": false
}

Configuration

Create ~/.openclaw/gpu_monitor_config.json:

{
  "update_interval_seconds": 2,
  "ollama_log_path": "/path/to/Ollama/server.log",
  "quiet_mode": false
}

Field	Type	Description
`update_interval_seconds`	int	Refresh interval (default: 2)
`ollama_log_path`	string	Path to Ollama server.log (optional)
`quiet_mode`	bool	Disable banner messages

Output Examples

With Ollama Layer Info

┌─[Update #1] 12:30:45
├─ GPU:         NVIDIA GeForce RTX 3080
├─ Memory Used: 8.5/10.0 GB (85.0%)
├─ Log Time:    [实时模式 - 无层数数据]
├─ GPU Layers:  [实时模式]

With Layer Data

┌─[Update #1] 12:31:02
├─ GPU:         NVIDIA GeForce RTX 3080
├─ Memory Used: 7.2/10.0 GB (72.0%)
├─ Log Time:    time=2026-03-27T12:31:02+08:00
├─ GPU Layers:  32 / 33
├─ CPU Layers:  1 (3.0%)

Without Ollama

┌─[Update #1] 12:32:15
├─ GPU:         NVIDIA GeForce RTX 3080
├─ Memory Used: 9.2/10.0 GB (92.0%)
├─ Log Time:    [实时模式 - 无层数数据]
├─ GPU Layers:  [实时模式]

Prerequisites

Python 3.7+
NVIDIA GPU with nvidia-smi available (Windows/Linux/macOS)
(Optional) Ollama server for layer tracking

License

MIT License

安全使用建议

This skill appears to do exactly what it says: local GPU monitoring via nvidia-smi and optional Ollama server.log parsing. Before installing: 1) Ensure you have an NVIDIA GPU and nvidia-smi available (SKILL.md requires this, but registry metadata omitted it). 2) Be careful what log path you supply—pointing the tool at arbitrary system logs could expose sensitive information; the skill will read the specified log file. 3) The package includes Python files that run locally and will write an entry.py file into the skill directory; inspect the files if you don't trust the unknown source. 4) Because the skill has no network behavior, running it locally is lower risk—still run it in a user account with appropriate file permissions and review the code if you want higher assurance.

功能分析

Type: OpenClaw Skill Name: gpu-monitor Version: 1.0.0 The skill provides GPU monitoring by executing nvidia-smi and reading Ollama logs, which is consistent with its stated purpose. However, entry.py contains a highly irregular and suspicious function (create_entry_script) designed to overwrite the script's own source code on disk with a new version. While this self-modification logic is not currently invoked in the main execution path, it represents a high-risk coding pattern often used for evasion or persistence. Additionally, the parsing logic in gpu_monitor.py uses non-standard regex for nvidia-smi output, suggesting the code is either poorly tested or designed for a very specific, non-standard environment.

能力评估

ℹ Purpose & Capability

The skill is implemented to monitor NVIDIA GPUs (calls nvidia-smi) and to optionally parse an Ollama server.log; those capabilities align with the name/description. Minor inconsistency: registry metadata lists no required binaries, but both SKILL.md and the code require nvidia-smi and Python. Requiring access to an Ollama server.log is coherent for the stated 'layer distribution' feature.

ℹ Instruction Scope

Runtime instructions and the code remain narrowly scoped: they run nvidia-smi, read a per-user config file (~/.openclaw/gpu_monitor_config.json) if present, and optionally tail/parse a user-specified Ollama server.log path (reads last ~50 lines). There are no network calls, remote endpoints, or attempts to read other system credentials. Note: parsing an arbitrary log file is potentially sensitive depending on what file the user points it at, but this behavior is directly tied to the declared feature.

✓ Install Mechanism

No install spec or remote downloads are provided; code files are bundled with the skill. No external archives or untrusted URLs are fetched or extracted by the skill.

ℹ Credentials

The skill requests no environment variables or credentials. It does read a config file in the user's home (~/.openclaw/gpu_monitor_config.json) and will read any log path provided by the user. This file access is proportional to the feature set, but the registry metadata could more accurately declare the dependency on nvidia-smi and the optional config/log path.

ℹ Persistence & Privilege

The skill does not request permanent/always-on privileges (always:false) and does not modify other skills or system-wide settings. A small surprising behavior: entry.py contains a helper that writes an entry.py file (self-overwrite/creation inside the skill directory). This is limited to the skill's directory and not evidence of privilege escalation, but users may want to be aware that the package can write files to its own installation folder.

如何使用

确保已安装 OpenClaw（本地或 Docker 部署）
在对话框中输入安装命令：/install gpu-monitor
安装完成后，直接呼叫该 Skill 的名称或使用 /gpu-monitor 触发
根据 Skill 的参数说明提供必要输入，即可获得结构化输出

版本历史

v1.0.0

元数据

Slug gpu-monitor

版本 1.0.0

许可证 MIT-0

累计安装 0

当前安装数 0

历史版本数 1

常见问题

gpu monitor 是什么？

Provides real-time NVIDIA GPU usage and memory stats, plus Ollama model layer GPU/CPU distribution via server.log parsing with live updates. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 92 次。

如何安装 gpu monitor？

在 OpenClaw 或 Claude Code 对话框中运行命令「/install gpu-monitor」即可一键安装，无需额外配置。

gpu monitor 是免费的吗？

是的，gpu monitor 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。

gpu monitor 支持哪些平台？

gpu monitor 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。

谁开发了 gpu monitor？

由 TinkerJueBerg（@tinkerjueberg）开发并维护，当前版本 v1.0.0。