remote-gpu-trainer

remote-gpu-trainer — Remote GPU Job Orchestration

Overview

Deploy and babysit long-running GPU jobs on rented boxes you don't own, across any platform, and get the result off the box before the meter or a preemption kills it. The core insight: 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, not to provision a cluster.

This skill is platform-agnostic at the core, platform-specific at the edges: a fixed set of operating principles + a 6-phase lifecycle that hold everywhere, plus one profile per platform (profiles/<platform>.md) that owns every concrete path, proxy, billing verb, and spot semantic. Its defensible value is the union the big orchestrators skip: Chinese cgroup-isolated rentals + bare-SSH cheap boxes + the disk-budget / monitoring / teardown reality that is the job on metered hardware.

When to Use This Skill

Use whenever the user deploys, trains, monitors, or troubleshoots a long-running GPU job on a RENTED or remote instance they do not own — training, eval, ablation sweeps, batch inference, or large data processing — on AutoDL, RunPod, vast.ai, Lambda, Paperspace, Chinese platforms (恒源云/矩池云/Featurize/ 揽睿星舟), a bare SSH box, Slurm, or Kubernetes; single OR multi-instance. Triggers (multilingual): "远程 GPU 训练", "GPU 租赁",