by Hanyuyuan6 · Codex Skill · ★ 59
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remote-gpu-trainer An Agent Skill for running long GPU jobs on machines you rent but don't own. Deploy, train, monitor, and tear down safely across AutoDL, RunPod, vast.ai, Lambda, Paperspace, the Chinese platforms (恒源云 / 矩池云 / Featurize / 揽睿星舟), bare SSH boxes, Slurm, and Kubernetes. One instance, or a fan-out of many. Disambiguation: "AutoDL" here is the autodl.com GPU-rental platform, not AutoML or NAS. And this is an Agent Skill — a with reference docs and script templates — not a CLI or an SDK. It rides above each platform's API and encodes the operational survival knowledge those APIs leave out. The whole skill is built on one mental model: you are a short-term tenant on someone else's machine. So it teaches tenant survival — detach the job, make the result outlive the box, stop the meter without losing data — and treats that as a single model across every backend. Only the per-platform specifics (stop
| Stars | 59 |
| Forks | 7 |
| Language | Shell |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 67.4575269899105/100 |
| Last Updated | 2026-06-21 |
| Created | 2026-06-18 |
| Platforms | claude-code, cli, codex |
| Est. Tokens | ~14k |
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remote-gpu-trainer is An Agent Skill for deploying, training, monitoring & tearing down long GPU jobs on rented/remote instances — AutoDL, RunPod, vast.ai, Lambda, Slurm, bare SSH.. It is categorized as a Codex Skill with 59 GitHub stars.
remote-gpu-trainer is primarily written in Shell. It covers topics such as agent-skills, autodl, claude-code.
You can find installation instructions and usage details in the remote-gpu-trainer GitHub repository at github.com/Hanyuyuan6/remote-gpu-trainer. The project has 59 stars and 7 forks, indicating an active community.
remote-gpu-trainer is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to remote-gpu-trainer on Agent Skills Hub include agent-skills, deterministic-agent-control-protocol, agent-designer. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.