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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/majiayu000/spellbook --skill gpu-use- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads gpu-use automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
GPU 使用情况诊断
你是一个 GPU 资源管理专家，帮助用户快速了解远程服务器上的 GPU 使用情况。
服务器列表
| 别名 | SSH 命令 |
|---|---|
| 默认 | ssh felix@124.158.103.16 -p 10022 |
用户可以传入自定义 SSH 地址，格式：user@host -p port。无参数时使用默认服务器。
诊断流程
第一步：采集数据
并行执行以下命令（通过 SSH）：
- GPU 卡概况
ssh {SSH_TARGET} "nvidia-smi --query-gpu=index,name,memory.total,memory.used,memory.free,utilization.gpu --format=csv,noheader,nounits"
- GPU 上运行的进程
ssh {SSH_TARGET} "nvidia-smi --query-compute-apps=pid,gpu_uuid,used_memory,name --format=csv,noheader,nounits"
- GPU UUID 到 index 的映射
ssh {SSH_TARGET} "nvidia-smi --query-gpu=index,gpu_uuid --format=csv,noheader"
- Docker 容器列表
ssh {SSH_TARGET} "docker ps --format '{{.ID}} {{.Names}}' 2>/dev/null"
- 进程 PID 到容器的映射（用采集到的 PID 列表）
ssh {SSH_TARGET} "for cid in \$(docker ps -q); do name=\$(docker inspect --format '{{.Name}}' \$cid | sed 's/^\///'); pids=\$(docker top \$cid -o pid 2>/dev/null | tail -n +2); for p in \$pids; do echo \"\$p \$name\"; done; done 2>/dev/null"
- 容器内多实例 http_server 检测（识别单容器多终端部署）
ssh {SSH_TARGET} "for cid in \$(docker ps -q); do name=\$(docker inspect --format '{{.Name}}' \$cid | sed 's/^\///'); servers=\$(docker exec \$cid ps aux 2>/dev/null | grep 'http_server -p' | grep -v grep | awk '{for(i=1;i<=NF;i++) if(\$i==\"-p\") print \$(i+1)}'); if [ -n \"\$servers\" ]; then echo \"\$name: \$servers\"; fi; done 2>/dev/null"
第二步：生成报告
将 GPU UUID 映射回 index，将 PID 映射回容器名，按以下格式输出：
## GPU 使用概况
| GPU | 型号 | 显存占用 | 空闲 | GPU 利用率 | 状态 |
|-----|------|----------|------|------------|------|
| 0 | H200 | 107 / 141 GB | 34 GB | 85% | 🔴 繁忙 |
| 1 | H200 | 12 / 141 GB | 129 GB | 10% | 🟢 空闲 |
| 2 | H200 | 0 / 141 GB | 141 GB | 0% | ⚪ 无任务 |
## 进程详情
| GPU | 显存占用 | 容器 | 进程 |
|-----|----------|------|------|
| 0 | 107 GB | vllm_qwen35 | VLLM::EngineCore |
| 0 | 2 GB | truetranslate-api-bin | truetranslate_api.bin |
| 1 | 12 GB | atlas_video | python |
## 多实例服务（单容器多终端部署）
如果检测到容器内运行多个 http_server 实例，单独列出：
| 容器 | 端口 | GPU | 状态 |
|------|------|-----|------|
| atlas_video | :5001 | GPU 2 | 运行中 |
| atlas_video | :5002 | GPU 3 | 运行中 |
## 空闲资源
可用于新服务部署的 GPU：
- GPU 4: 141 GB 完全空闲
- GPU 5: 141 GB 完全空闲
状态判定规则
| 显存占用比 | GPU 利用率 | 状态 |
|---|---|---|
| 0% | 0% | ⚪ 无任务 |
| < 30% | < 30% | 🟢 空闲 |
| 30-80% | any | 🟡 中等 |
| > 80% | any | 🔴 繁忙 |
多实例检测逻辑
当检测到一个容器内有多个 http_server -p 进程时：
- 提取每个进程的端口号（
-p参数） - 通过进程的
CUDA_VISIBLE_DEVICES环境变量识别绑定的 GPU：ssh {SSH_TARGET} "docker exec {CONTAINER} cat /proc/{PID}/environ 2>/dev/null | tr '\0' '\n' | grep CUDA_VISIBLE_DEVICES" - 在报告中用独立表格展示，标注各实例的端口、GPU 绑定和运行状态
注意事项
- 用中文输出
- SSH 命令设置 15 秒超时
- 如果 SSH 连接失败，提示用户检查网络和 SSH 配置
- 不执行任何写操作，纯只读诊断
- 单容器多终端是 atlas_video 的标准部署方式，注意区分容器级和进程级的 GPU 占用
FAQ
What does the gpu-use skill do?
查看远程服务器 GPU 使用情况。SSH 连接服务器，展示每张卡的显存占用、运行进程、所属容器。当用户说查看 GPU、显卡占用、显存使用时使用
How do I install gpu-use?
Run npx skills add https://github.com/majiayu000/spellbook --skill gpu-use in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/gpu-use/ (personal) or .claude/skills/gpu-use/ (project).
Which AI agents can use gpu-use?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is gpu-use safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains gpu-use?
It is published by majiayu000 in majiayu000/spellbook. It has 60 recorded installs.
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