# remote-gpu-trainer

An Agent Skill for the **whole life of a DL experiment** — **RUN** a job (on a GPU you own *or* rent), **VERIFY** that the number is real, and **DELIVER** it as organized, single-source, reproducible figures and tables. Its deepest part is still remote-GPU operations (AutoDL, RunPod, vast.ai, Lambda, Paperspace, the Chinese platforms 恒源云 / 矩池云 / Featurize / 揽睿星舟, bare SSH, Slurm, Kubernetes — one instance or a fan-out of many), now wrapped in the full run → verify → deliver arc.

[](LICENSE) [](https://agentskills.io) [](evals/RESULTS.md) [](#whats-inside) [](#whats-inside) [](https://github.com/Hanyuyuan6/remote-gpu-trainer/releases/tag/v1.0.0)

> **What this is, and what it isn't.** "AutoDL" here means the [autodl.com](https://www.autodl.com) > GPU-rental platform, not AutoML or NAS. And this is an Agent Skill — a `SKILL.md` with reference docs and > script templates, not a CLI or an SDK. It sits on top of each platform's API and the DL workflow, and > captures the operational and judgment knowledge they leave out.

One skill, three phases of one real workflow:

- **RUN** — get a long GPU job to start, survive, and finish, then get the result off the box. On a machine **you own** there is no meter; on a **rented** one the core insight is that you are a short-term tenant on someone else's machine — so the job is to detach the work, make the result outlive the instance, and stop the meter safely. The things that genuinely vary between platforms (stop-vs-destroy billing, machin