remote-gpu-trainer

# remote-gpu-trainer — the DL Experiment Lifecycle ## Overview One skill for the whole arc of a DL experiment: **RUN → VERIFY → DELIVER.** - **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** box 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*, not to provision a cluster. Platform-specific at the edges (one `profiles/<platform>.md` owns every path, proxy, billing verb, and spot rule), invariant at the core. - **VERIFY** — *is this number a bug, a real effect, or noise?* A surprising result is a hypothesis, not a fact to report. Platform-agnostic. - **DELIVER** — organize the result so every shipped number/figure/table is a *deterministic function of one immutable evidence layer*; provenance and cross-document consistency are locked by mechanism, not by a human remembering to update three documents. Platform-agnostic. Two stances run through VERIFY and DELIVER: **user sovereignty** (the science — seed count, which samples, whether an `aux` channel exists — is the user's call; the skill organizes and discloses a tradeoff *once*, then stops nagging) and **audit → disclose, not enforce** (the skill is an honest auditor, not a gate guard — an integrity issue must surface *with the conclusion it affects*, but the skill never bl...