remote-gpu-trainerlisted

# 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 NOT to use — and what to use instead | Situation | Use instead | |---|---| | Local single-GPU, or multi-GPU **DDP inside one box** | `torchrun` / `accelerate` directly | | Managed multi-cloud price-shopping + auto spot-recovery across **Western** clouds | **SkyPilot** (has its own Agent Skill) — then come back here to make your *code* resume-correct so its recovery actually works | | Open BYOC dev environments | **dstack** | | Zero-ops serverless inference | **Modal** | | "Is this metric / ablation delta real?" | **REQUIRED:** `verifyin