DL实验生命周期的代理技能：RUN（你拥有或租用的GPU）→验证数字真实→交付可复现的单一来源数据和表格。
概览
这是深度学习实验中贯穿始终的代理技能：在你拥有或租用的GPU上完成任务，数字是真实的，并且是单一来源、可复现的图形和表格。它最深层的部分是远程GPU操作——AutoDL、RunPod、vast.ai、Lambda、Paperspace、中国平台（恒源云 / 矩池云 / Featurize / 揽睿星舟）、裸SSH、Slurm、Kubernetes、一台主机或众多扇形系统——包裹在完整的运行→验证→交付弧线中。 这里的“AutoDL”是 autodl.com 的GPU租赁平台，不是AutoML。这是一种技能——带有参考文档和脚本模板的 SKILL.md，而不是CLI或SDK。它建立在每个平台的API之上，捕捉文档中遗漏的操作和判断知识。 - ——先完成一个长的GPU任务，开始、存活并完成，然后从盒子里拿出结果。在租用机器上，你是别人硬件的短期租户：脱离工作，让结果比实例更久，并安全停止计量。
概述（AI 翻译）
用你的编码代理在租用的GPU上训练，避免丢失检查点或支付闲置机器费用。
英语 ·中文简介
租一块显卡是最简单的部分。昂贵的惊喜则会在后面出现：拆解导致你唯一的检查点副本被删除，一台“停止”的机器继续计费，一项看似忙碌但几个小时前就出错的工作。
remote-gpu-trainer 是针对 Claude Code、Codex 及其他编码代理的 Agent 技能。它允许你的代理在 AutoDL、RunPod、vast.ai、Lambda、Paperspace、其他四个中国 GPU 云或任何可 SSH 的机器上启动、监控、恢复训练，并带回经过验证而非复制的检查点。
它所阻止的
- 在拆解时丢失检查点。 释放 AutoDL 实例或终止 RunPod Pod 会删除其磁盘。代理不会释放任何文件，直到每个文件都被独立读取、哈希、加载并重新评分，并且你表示同意。
- **为空闲机器付费。**停止运行的RunPod Pod以运行速率的两倍计费，停止 vast.ai 实例会无限计费其磁盘，而Lambda上的
sudo shutdown则保持计量器运行。技能知道哪个动作实际上会停止每个计量表。
本页由 AI 翻译，技术细节请以原文为准。
README
以下为原文 README 节选，完整内容请查看原文。
remote-gpu-trainer
Train on rented GPUs with your coding agent, without losing checkpoints or paying for idle machines.
English · 中文简介
Renting a GPU is the easy part. The costly surprises come later: a teardown that deletes the only copy of your checkpoints, a “stopped” machine that keeps billing, a job that looks busy but went wrong hours ago.
remote-gpu-trainer is an Agent Skill for Claude Code, Codex and other coding agents. It lets your agent launch, monitor, resume and stop training on AutoDL, RunPod, vast.ai, Lambda, Paperspace, four other Chinese GPU clouds or any machine you can SSH into, and bring back checkpoints that are verified, not just copied.
What it prevents
- Losing checkpoints at teardown. Releasing an AutoDL instance or terminating a RunPod Pod deletes its disk. The agent releases nothing until every file has been read back somewhere independent, hashed, loaded and re-scored, and you have said yes.
- Paying for idle machines. A stopped RunPod Pod bills its volume at twice the running rate, a stopped vast.ai instance bills its disk indefinitely, and
sudo shutdownon Lambda leaves the meter running. The skill knows which action actually stops each meter. - Silent timers. AutoDL releases a stopped instance after 15 days, data disk included, and RunPod and vast.ai can delete instances and storage when your balance runs out. The agent tells you about these the first time you use a platform.
- Runs that look alive but are not learning. A live process and a scrolling log are not health. Status reports compare the metric with a reference run at the same epoch and say how old the data is.
- OOM, NaN, hangs and all-zero metrics. OOM on step 2, NaN after a precision change, one crashed rank hanging the rest, a resume that restarts at epoch 0, a starved GPU, mAP stuck at zero while the loss falls: each is looked up by symptom, with its root cause and fix.
- Numbers that are not real. Before a result is reported, the agent states the seed, changes one variable at a time, checks for leakage and decides whether a gap is a bug, a real effect or noise.
Install
Let your agent do it. Paste this into Claude Code, Codex, Cursor, Gemini CLI or GitHub Copilot:
Install the remote-gpu-trainer agent skill. Read
https://raw.githubusercontent.com/Hanyuyuan6/remote-gpu-trainer/main/README.md
and follow its "For AI agents" section for this host. Then tell me
where you installed it and how you confirmed it loaded.
Or clone it yourself. For Claude Code:
git clone https://github.com/Hanyuyuan6/remote-gpu-trainer.git \
~/.claude/skills/remote-gpu-trainer
推荐工具
换一个关键词，或者移除筛选条件。
安装
npx skillfish add hanyuyuan6/remote-gpu-trainer