zytsyj/dsh-gpu
DeepSeek Harness 的 GPU 感知执行层:gpu_status / gpu_exec / gpu_run_bg 工具,自动显卡选择,逐步 GPU 上下文。
Project Overview项目介绍
dsh-gpu is a GPU-aware plugin for DeepSeek Harness, exposing gpu_status, gpu_exec, and gpu_run_bg tools that run through the mounted shell executor. It auto-picks the freest card or honors an explicit pin, and optionally injects a per-step GPU snapshot. Use it for multi-GPU training, inference servers, and benchmarks. Caveat: selection is advisory—concurrent agents may pick the same card; pin gpuIndex for exclusive claims.
dsh-gpu 是 DeepSeek Harness 的 GPU 感知执行插件,提供 gpu_status、gpu_exec、gpu_run_bg 三个工具,命令通过挂载的 shell 执行器运行,支持自动选卡(优先空闲)或显式固定,并可在每步注入 GPU 快照。适用于多卡训练、推理服务与基准测试等场景。注意选卡为建议性,多 agent 并发仍可能撞卡,专用请固定 gpuIndex。
请帮我安装这个 DSH 插件。安装前先完成【兼容性检查 + 安全性检查】,检查通过再动手。
插件:dsh-gpu(zytsyj/dsh-gpu)
仓库:https://github.com/zytsyj/dsh-gpu
本站详情页:https://www.yhbd.top/plugins/zytsyj-dsh-gpu/
本站登记:类型 plugin · 归类 原生 DSH 插件 · 许可证 MIT · ⭐ 2 · 最近提交 2026-08-14 · 主语言 TypeScript
按下面顺序执行,每步先把结论告诉我,再进入下一步:
【1 兼容性检查】
① 我这边:DSH 版本、Node 版本、操作系统、当前 profile(web / desktop)。
② 读它的 README、package.json、插件 manifest,列出它要求的 DSH 版本 / Node 版本 / 操作系统 / 外部依赖 / 需要另外先装的运行时。
③ 逐条比对,结论只写「满足 / 不满足 / 未知」三种;不满足的给出可行替代方案。
④ 检查是否和我已装的插件冲突:命令名重复、skill / tool 重名、端口占用、重复注册的 MCP server。
【2 安全性检查】
① 仓库可信度:和上面「本站登记」是否一致;star / fork 数、创建时间、最近提交,是否归档或长期停更。
② 安装脚本:逐行看 package.json 的 preinstall / install / postinstall,以及 install.sh、setup.ps1 之类脚本。出现 curl|bash、下载后直接执行、混淆代码、访问与插件功能无关的域名,立刻停下来告诉我,不要继续装。
③ 依赖:列出新增依赖,标出无人维护、或与知名包拼写近似的可疑包(typosquatting)。
④ 权限与副作用:它会读写哪些目录、访问哪些域名、需要哪些 DSH 权限(filesystem / network / shell / clipboard 等),以及怎么卸载和回滚。
⑤ 如果它要求 sudo / 管理员权限,或权限明显超出功能所需,先停下来问我。
【3 安装】
上面两步没有「不满足」和「高危项」时才执行;用官方推荐方式安装,不要自行提权。
【4 汇报】
用表格输出:检查项 / 结论 / 依据 / 是否需要我决策。拿不准的一律写「未知」并说明要我怎么确认——不要猜,也不要替我决定。
Send this message to DSH in your current session: it verifies compatibility and security first (answering met / not met / unknown item by item) and only installs once everything checks out — it will stop and ask you if it finds a high-risk item. The box scrolls; the copy is the full prompt. CLI install commands may not be accurate across systems, so DSH is the safer route.把上面这条消息直接发给当前会话里的 DSH:它会先核对兼容性与安全性(逐条给「满足 / 不满足 / 未知」),确认没问题再安装,有高危项会停下来问你。框内可滚动,复制到的是完整提示词;安装命令不一定准确,发给 DSH 更稳。
- Only 2 stars - very few users, little community feedback星标只有 2,几乎没人在用,遇到问题缺少社区反馈
DSH walks through these 9 checksDSH 会逐条核对这 9 项
Compatibility兼容性
- DSH, Node, OS and profile requirementsDSH 版本 / Node 版本 / 操作系统 / profile 是否满足要求
- External dependencies and runtimes (Electron / Python / Docker, ...)外部依赖与运行时(Electron / Python / Docker 等)是否齐备
- Conflicts with installed plugins: command names, skill / tool names, ports, duplicate MCP registration与已装插件是否冲突:命令名、skill / tool 重名、端口占用、重复 MCP 注册
Security安全性
- Repo matches the facts registered here; archived or abandoned?仓库是否与页面登记一致,是否归档或长期停更
- Safety of preinstall / install / postinstall and install.sh / setup.ps1preinstall / install / postinstall 与 install.sh、setup.ps1 是否安全
- curl|bash, download-then-execute, obfuscation, unrelated domains → stop immediatelycurl|bash、下载即执行、混淆代码、无关域名 → 立刻停止
- Typosquatting or unmaintained packages among the new dependencies新增依赖里有没有 typosquatting 或无人维护的包
- Requested permissions vs. what the feature actually needs申请了哪些权限、是否超出功能所需(filesystem / network / shell / clipboard)
- Any sudo / admin requirement, plus uninstall and rollback是否要求 sudo / 管理员权限,以及卸载与回滚方式
Anything uncertain must be marked unknown with a note on how to confirm it. This site's signal screen is a static snapshot, not a security audit.拿不准的必须标「未知」并说明要我怎么确认。本站的信号筛查是静态快照,不能替代安全审计。
Or use CLI install (for developers)或使用命令行安装(适合开发者)
CLI Install命令行安装
dsh plugin --profile web add dsh-gpu
把 zytsyj/dsh-gpu 加入你的 DSH 配置(web profile)即可启用。
READMEREADME
dsh-gpu
GPU-aware execution layer for DeepSeek Harness (dsh). Out-of-tree plugin; no harness patches required.
Agents get three tools — gpu_status, gpu_exec, gpu_run_bg — plus an optional per-step GPU context line. Cards are selected automatically (freest first) with CUDA_VISIBLE_DEVICES set in the command environment; pin a card explicitly when you care.
8 GPU(s), free: [0,1,2,3,4,5,6,7]
GPU0 Tesla V100-SXM2-32GB: 4264/32768MiB 0%util 40C
...
[gpus 1 — GPU 1 (auto: freest 1)] exit 0
How it works
gpu_status— one query, every device: memory used/total, SM utilization, temperature, and a free/busy verdict. A device is busy at or above 80% memory used or 50% utilization (both configurable).gpu_exec— one-shot command with a selected card:CUDA_VISIBLE_DEVICES=<freest>is passed through the mountedctx.shellexecutor's environment. Auto-select or pingpuIndex; selectcountcards for multi-GPU commands.gpu_run_bg— long-running GPU jobs (training, inference servers, benchmarks) register as agpujob inctx.jobs: returns a job id immediately, read withjob_output, stop withjob_kill.- Per-step context (optional, on by default) — injects a one-line GPU snapshot into eligible steps (the
time-contextpattern), rate-limited to one sample per minute.
All execution rides the mounted shell executor. Local host, or any remote execution world (e.g. an SSH provider plugin) — dsh-gpu doesn't know or care where the GPUs are; it queries and launches through the same seam the bash tool uses.
Install
dsh-gpu is an out-of-tree bundle plugin. Install and activate it in a profile with the official plugin command:
dsh plugin --profile <name> add dsh-gpu
The package's bundled cordis.patch.yml registers the plugin automatically. To override its configuration, add an entry with the same id to the profile's cordis.patch.yml:
- insert:
- id: gpu
name: dsh-gpu
config:
stepContext: true
Load order note: place it after your execution-world plugins (e.g. an SSH provider) so the shell seam it queries is the one you intend.
Showing the opening section of the README — the full document lives in the repository以上为 README 开头摘要,完整文档在仓库内 · View the full README on GitHub →在 GitHub 查看完整 README →
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