{"slug":"hanyuyuan6-remote-gpu-trainer","name":"Remote Gpu Trainer","description":"A SKILL.md-based agent skill covering the full deep learning experiment lifecycle—running jobs on owned or rented GPUs, verifying results, and delivering reproducible figures and tables.","long_description":"---\nname: \"remote-gpu-trainer\"\ndescription: \"Use when running, debugging, verifying, or delivering a deep-learning experiment on an owned or rented GPU, especially AutoDL or a remote SSH host; also use for Windows + Clash/Mihomo high-port SSH banner timeouts, fake-IP, or TUN routing interference. Covers launch, checkpoint/resume, detached monitoring, OOM/NaN/convergence/data-loader failures, multi-GPU hangs, ablations, result verification, pull/teardown safety, and canonical export closure. Routes durable replicas to mirror-research-artifacts. Triggers: owned/rented GPU, AutoDL, SSH, Windows Clash/Mihomo, banner timeout, fake-IP, TUN, train/debug/verify/pull/export, 远程GPU训练/租卡, 断点续训, 消融复现, checkpoint 拉回.\"\nlicense: MIT\nmetadata:\n  last-model-review: \"2026-08-10 AutoDL canonical export and generic mirror handoff; preserves 2026-07 lifecycle review findings (day completed from aca1c467, which rewrote the description and added the Windows/Clash SSH gate — the bare 2026-08 form is unparseable to the staleness hook)\"\n---\n\n# remote-gpu-trainer — the DL Experiment Lifecycle\n\n## Overview\n\nOne skill for the whole arc of a DL experiment: **RUN → VERIFY → DELIVER.**\n\n- **RUN** — get a long GPU job to start, survive, and finish, then get the result off the box. On a\n  machine **you own** there is no meter; on a **rented** box the core insight is that **you are a\n  short-term tenant on someone else's machine** — so the job is to *detach the work, make the result\n  outlive the instance, and stop the meter safely*, not to provision a cluster. Platform-specific at the\n  edges (one `profiles/<platform>.md` owns every path, proxy, billing verb, and spot rule), invariant at\n  the core.\n- **Remote ownership boundary** — this skill is the **compute/control layer**: it binds inputs, runs and\n  verifies compute, and closes one run into `export/<run-id>`. Long-term project organization belongs to\n  `research-artifact-hygiene`; any durable/local/cloud mirror begins only at a validated export and belongs\n  to the generic `mirror-research-artifacts` skill. Never mirror a mutable `active/` tree.\n- **VERIFY** — *is this number a bug, a real effect, or noise?* A surprising result is a hypothesis, not\n  a fact to report. Platform-agnostic.\n- **DELIVER** — organize the result so every shipped number/figure/table is a *deterministic function of\n  one immutable evidence layer*; provenance and cross-document consistency are locked by mechanism, not\n  by a human remembering to update three documents. Platform-agnostic.\n\nTwo stances run through VERIFY and DELIVER: **user sovereignty** (the science — seed count, which samples,\nwhether an `aux` channel exists — is the user's call; the skill organizes and discloses a tradeoff *once*,\nthen stops nagging) and **audit → disclose, not enforce** (the skill is an honest auditor, not a gate\nguard — an integrity issue must surface *with the conclusion it affects*, but the skill never blocks the\nuser from shipping). Mantra: **\"disclose it, or don't claim it.\"**\n\n## Route first\n\n1. **RUN — own the box or rent it?**\n   - **Local** (a workstation/laptop you own, no meter) → `references/run-local/` and `profiles/local.md`.\n   - **Rented / remote** (any metered or shared box you don't own) → `references/run-remote/`, and pick\n     your **`profiles/<platform>.md`** FIRST (it owns every path/verb/proxy the phases delegate to).\n2. **Then ALWAYS** → **VERIFY** the result (`references/verifying/`). A green run is not a real number.\n3. When publication synthesis is requested, optionally consult `references/delivering/` for\n   **legacy/non-canonical publication synthesis guidance**. It never defines the artifact layout: canonical\n   runs, hardware evidence, trust records, figures and tables belong to `research-artifact-hygiene`.\n\n> Already debugging a model that won't converge / OOMs / hangs / NaNs, regardless of where it runs? Jump\n> straight to **`references/training/`** (the 8-file debug layer), then come back to VERIFY before you report.\n\n## Operating principles (the spine)\n\nThe load-bearing invariants. One line each; the full cross-platform set (10 invariants for the remote\nlifecycle) is in **`references/run-remote/principles.md`** — read it before Phase 0 of a remote run.\n\n- **Checkpoint-to-durable + idempotent resume is the universal spine.** File-checkpoint to the durable\n  location + unconditional load-latest-on-startup is the *one* mechanism that survives an SSH drop, a\n  Slurm walltime kill, a K8s reschedule, a spot preemption, a Colab disconnect. The detach primitive\n  (tmux / sbatch / Job) is the swappable plug; this is the invariant.\n- **Trust the artifact you loaded, not a log line that claims success.** \"synced / saved / done\" lies\n  under a silently-failed write; a watcher's own state is also a claim — reconcile it against the real\n  process / artifact / pixels / bytes.\n- **Cheap checks before expensive compute.** A 1–2 batch CPU smoke (logger off) kills import/config/\n  shape/scale bugs for ~free, before they bill GPU-hours.\n- **Cost and destructive actions are the user's call.** Never auto-release/terminate, never delete durable\n  files without confirmation; if cleanup can't free space, ask to expand the disk, don't silently shrink\n  the experiment.\n- **Execution permission is not task authority.** Sandbox / Full Access only controls whether a command\n  can run. Keep operational authority and scientific promotion separate: once a bounded, non-overwriting\n  delivery objective is authorized, same-scope diagnostics, verifier/schema version bumps, tests, hashes,\n  and control-plane repairs do **not** require a fresh confirmation merely because they mint a new immutable\n  ID. Re-ask for new billable compute, destructive/irreversible actions, science-protocol changes, metric\n  promotion, publication, or any other material scope expansion. A standing unattended contract applies\n  only after its own activation rule is satisfied.\n- **Make the control plane cheap and the data plane rare.** Validate small schemas, identities, paths, and\n  contract hashes before rereading multi-GB bundles. A synthetic fixture may test rejection behavior but\n  may never invent the producer's positive schema; freeze a redacted real-shape fixture and prove its test\n  is live. Recompute every large payload once per trust boundary—producer, independent remote acceptance,\n  and local pull—not once per wrapper or verifier revision.\n- **One-way run closure.** Mutable work stays under `active/<run-id>`; only a validated capsule may move\n  from `export/.partial/<run-id>` to `export/<run-id>`, and failed closeouts move to `quarantine/`.\n  AutoDL binds this exactly as\n  `/root/autodl-tmp/<project>/{cache,active,export/.partial,export/<run-id>,quarantine}`. A closed export is\n  fully isomorphic to canonical local `runs/<run-id>`: `run.json`, `config.yaml`, `train.csv`, `best.pth`,\n  optional frozen `last.pth`, and `test/<test-id>/{metrics.json,results.parquet,vis/<condition-id>/<task-native-role>/<sample-id>.png}`.\n  Every declared software test must include visualization coverage for all declared conditions × task-native\n  roles × K fixed selected samples. Real capture/hardware results never enter this software capsule; close\n  them separately as `export/hardware/<hardware-run-id>` (capture/decode/model-run bindings, no copied\n  weights) or hand them to `research-artifact-hygiene`. A hardware test without machine-readable ground truth\n  must declare that status and metric non-applicability; it keeps finite-forward rows and prediction/overlay\n  visuals but never invents ground truth or GT-derived metrics. Caches and whole active trees never cross a\n  mirror boundary. Layout and gates → `references/run-remote/artifact-layout.md`.\n- **Before teardown, prove the evidence outlives the host.** Teardown is irreversible and *\"I scp'd it\n  back\"* is just another log line. The gate is not \"files copied\" but **\"every number I reported\n  re-reads from the local copy\"** — diff each claim against the pulled artifact, then write a\n  provenance note *next to the data* (protocol, reference frame, caveats) so a later reader can\n  retrace it without the chat log. Two traps: (1) what you did **not** pull is a decision, not an\n  oversight — say which (checkpoints are usually re-derivable from config+seed *if* determinism is\n  established; results are not); (2) **re-read the inventory, don't trust its prose** — a note saying\n  \"none of these are local\" may mean *none was trained here*, not *none is stored here*; the two differ\n  by everything when you are about to press destroy.\n- **Audit → disclose, not enforce.** What is mandatory is *disclosure*, not the *fix*. An integrity\n  finding (no disjoint val, leakage, test touched during selection, a number you can't re-derive) must\n  ride *with* the conclusion — but the skill discloses, it does not block.\n\n## RUN — local (a box you own)\n\nNo meter, no teardown clock — the risks move from *money* to *resource contention and your machine's\nstability*. The discipline that does **not** relax: env hygiene, resource awareness, artifact/checkpoint\ncare, and \"state the seed.\" Start at `profiles/local.md`, then the matching doc:\n\n- **Env hygiene** — never train/install in conda `base` on a persistent box; the 4-step gate (enumerate →\n  pick the project env → confirm `sys.executable` → run) → `references/run-local/env-hygiene.md`.\n- **Launch & detach** — nohup/tmux, log + alive probe, don't foreground-block → `references/run-local/launch.md`.\n- **Single-node multi-GPU** — `torchrun`/`accelerate` DDP env contract, the first-run rank/hang basics →\n  `references/run-local/multi-gpu.md` (multi-*node* → `references/run-remote/multinode.md`).\n- **Local OOM** — the fit-it ladder on hardware you can't rent bigger → `references/run-local/local-oom.md`.\n\n## RUN — remote (a box you rent)\n\n**Pick your profile FIRST** — it binds every concrete path/proxy/credential/billing verb/spot rule the\nphases delegate to. Mental verb model (one API across platforms; the profile binds each verb to real\ncommands): `up` (rent+reach) → `push` (code/data on) → `run` (detached + checkpointing) → `watch`\n(durable monitor) → `pull` (results off + verify) → `down` (stop the meter).\n\n**Windows + Clash/Mihomo high-port SSH gate.** Use **OpenSSH direct first** with strict host-key\nchecking. A **Paramiko fallback** is allowed only after recorded `banner_timeout`, `fake_ip`, or\n`tun_interference` evidence; it must use a DoH-selected address and Windows `IP_UNICAST_IF` on the\nsingle socket handed to the SSH transport. Never treat authentication failure, host-key mismatch,\nconnection refusal, or an unexplained error as proxy evidence. Never mutate system routes, DNS,\nproxy settings, or Clash/Mihomo configuration. Once fallback is authorized, you **must not report**\nthe host unreachable or a live refresh blocked before a bounded Paramiko single-socket attempt completes\nor host identity fails closed. A **transport failure proves only transport unavailability**; it never\nproves that a remote run is completed, live, failed or stalled. Full parameterized decision ladder and\noffline planner → `references/run-remote/ssh_transport.md` §4A.\n\n| You're on… | Profile | Meter-stop verb (the trap) |\n|---|---|---|\n| AutoDL (deepest, battle-tested) | `profiles/autodl.md` | 关机 stops meter, **keeps disk** (the AutoDL exception) |\n| RunPod | `profiles/runpod.md` | **terminate** (stop still bills 2×; destroys volume disk) |\n| vast.ai | `profiles/vastai.md` | **destroy** (stop bills disk forever) |\n| Lambda | `profiles/lambda.md` | **terminate** (no stop state) |\n| Paperspace | `profiles/paperspace.md` | **destroy + release IP + delete storage** |\n| 恒源云 / 矩池云 / Featurize / 揽睿星舟 | `profiles/china.md` | per-platform (data disk often bills while stopped) |\n| Bare SSH / Slurm / K8s / Colab | `profiles/generic-ssh.md` | **manual** (a forgotten box bills 24/7) |\n\n**The 6-phase lifecycle** (full per-platform checklist → `references/run-remote/lifecycle_ch","tagline":"An Agent Skill for the DL experiment lifecycle: RUN (a GPU you own or rent) → VERIFY the number is real → DELIVER reproducible, single-source figures and tables.","category":"research","tags":["agent-skills","deep-learning","gpu-training","reproducibility","remote-compute"],"author":"Hanyuyuan6","verified":false,"attribution":{"status":"community_indexed","statusLabel":"Community indexed","shortLabel":"COMMUNITY INDEXED","sourceLabel":"OpenAgentSkill auto-indexer","sourceDetail":"Hanyuyuan6/remote-gpu-trainer","creatorName":"Hanyuyuan6","creatorUrl":"https://github.com/Hanyuyuan6","sourceUrl":"https://github.com/Hanyuyuan6/remote-gpu-trainer/blob/main/SKILL.md","indexedBy":"OpenAgentSkill community index","claimUrl":"https://www.openagentskill.com/skills/hanyuyuan6-remote-gpu-trainer#claim-this-skill","claimCta":"Claim this skill","trustNote":"This listing was indexed from public sources and is not marked official until a maintainer claim is approved.","publicNote":"Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals."},"stats":{"stars":63,"forks":6,"verified_installs":0,"successful_runs":0,"total_outcomes":0,"rating":0,"review_count":0,"quality_score":47.24},"quality":{"score":73,"tier":"strong","label":"Strong","summary":"Solid option that is likely worth shortlisting for production workflows.","signals":[{"label":"GitHub stars","value":"63","tone":"neutral"},{"label":"Freshness","value":"1mo ago","tone":"positive"},{"label":"Install ready","value":"Yes","tone":"positive"},{"label":"License","value":"MIT","tone":"neutral"}],"warnings":[]},"trust":{"version":"trust-score-v5","score":64,"base_score":72,"outcome_confidence":0,"tier":"review","label":"Sandbox only","summary":"Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.","recommendedAction":"Run only in a sandbox and compare close alternatives before using it for real work.","decision":{"install_policy":"human_review_before_install","auto_install_allowed":false,"human_review_required":true,"sandbox_first":true,"agent_action":"Compare alternatives before installing.","reasoning":["64/100 Trust Score v5","72/100 Trust Score v4 baseline","Needs more real agent outcomes before unattended install","Install path is available","Review before production"],"review_required_when":["The workspace contains production secrets, payments, private customer data, or irreversible actions.","The install command requests shell, network, credential, database, or broad filesystem access.","Outcome evidence is missing, recently failed, or required human review.","Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace"]},"dimensions":[{"id":"github_adoption","label":"GitHub adoption","score":48,"weight":0.13,"status":"warn","detail":"63 GitHub stars"},{"id":"repo_activity","label":"Stars/forks activity","score":43,"weight":0.08,"status":"warn","detail":"63 stars, 6 forks; issue activity unavailable in current metadata"},{"id":"maintenance","label":"Recent maintenance","score":88,"weight":0.14,"status":"pass","detail":"1mo since push"},{"id":"license","label":"License clarity","score":86,"weight":0.09,"status":"pass","detail":"MIT"},{"id":"documentation","label":"README/SKILL.md completeness","score":100,"weight":0.14,"status":"pass","detail":"Metadata includes enough usage and workflow context"},{"id":"dependency_risk","label":"Dependency/runtime risk","score":64,"weight":0.12,"status":"info","detail":"credential or environment access, network or browser surface"},{"id":"installability","label":"Install availability","score":92,"weight":0.1,"status":"pass","detail":"npx skills add Hanyuyuan6/remote-gpu-trainer"},{"id":"install_safety","label":"Install command safety","score":92,"weight":0.1,"status":"pass","detail":"standard package or runtime install path"},{"id":"permission_surface","label":"Permission surface","score":18,"weight":0.07,"status":"fail","detail":"secrets or environment access, shell or command execution"},{"id":"repository","label":"Repository evidence","score":86,"weight":0.04,"status":"pass","detail":"https://github.com/Hanyuyuan6/remote-gpu-trainer/blob/main/SKILL.md"},{"id":"review_status","label":"Review status","score":88,"weight":0.05,"status":"pass","detail":"AI review data available"},{"id":"agent_outcomes","label":"Agent Proven outcomes","score":54,"weight":0.13,"status":"info","detail":"No agent outcome data yet"}],"checks":[{"status":"warn","label":"GitHub adoption","detail":"63 GitHub stars"},{"status":"warn","label":"Stars/forks activity","detail":"63 stars, 6 forks; 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None guarantees runtime safety."},"skill":{"slug":"hanyuyuan6-remote-gpu-trainer","name":"Remote Gpu Trainer","description":"A SKILL.md-based agent skill covering the full deep learning experiment lifecycle—running jobs on owned or rented GPUs, verifying results, and delivering reproducible figures and tables.","category":"research","url":"https://www.openagentskill.com/skills/hanyuyuan6-remote-gpu-trainer","repository":"https://github.com/Hanyuyuan6/remote-gpu-trainer/blob/main/SKILL.md","github_repo":"Hanyuyuan6/remote-gpu-trainer"},"suited_tasks":["Research agents workflows","Claude Code teams","builders willing to evaluate younger projects","Search sources","Extract claims","Synthesize findings","Research a market","Compare multiple sources"],"suited_agents":["Python","Codex","Claude Code","Cursor","OpenAgentSkill CLI","CLI"],"install":{"source_evidence":{"status":"source-recorded","sourceRecorded":true,"canOfferInstall":true,"path":"SKILL.md","revision":"892e5674ccf3cc36ed81715c4ebc4da0be2ecfa7","notice":"A skill instruction path and install command are recorded. This is not proof of compatibility, runtime success or safety; review the source and permissions first."},"command":"npx skills add Hanyuyuan6/remote-gpu-trainer","ready":true,"targets":[{"id":"openagentskill-cli","label":"CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.3.0/openagentskill-0.3.0.tgz add hanyuyuan6-remote-gpu-trainer"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"Remote Gpu Trainer\" agent skill from https://github.com/Hanyuyuan6/remote-gpu-trainer/blob/main/SKILL.md. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: A SKILL.md-based agent skill covering the full deep learning experiment lifecycle—running jobs on owned or rented GPUs, verifying results, and delivering reproducible figures and tables. 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