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", "GPU rental", "租卡", "spot 抢占", "spot preemption", "断点续训", "resumable training", "tmux 训练守护", "防 SSH 断线", "scp/rsync 上传", "多实例 ablation", "远程 GPU 监控", "省钱关机/销毁实例", "stop vs terminate billing", "checkpoint 磁盘满", "CUDA OOM/显存不足", "loss NaN/loss spike", "loss 不下降/不收敛", "overfit 单 batch", "FSDP/DeepSpeed 配置", "多卡训练 hang", "dataloader worker/数据增广 bug". **NOT** for purely local single-GPU training, in-instance multi-GPU DDP (use torchrun/accelerate), managed multi-cloud price-shopping (use SkyPilot's skill), or zero-ops serverless (use Modal). ## 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:** `verifying-dl-experiments` (this skill owns *running* the job; that one owns *whether the number is true*) | **This skill is for the blind spot those tools leave:** AutoDL + Chinese platforms, bare SSH/Slurm/K8s rentals, and the operational gotchas (inode caps, mirror stalls, cgroup OOM, silent sync, spot grace windows, irreversible teardown) that survive whichever provisioner you use. ## Operating principles (the WHY — 10 invariants) These hold on every metered, isolated, rented GPU; only the paths/CLI change. One line each; the deep form with cross-platform nuance is in **`references/principles.md`** (read it before Phase 0). 1. **Minimize paid wall-clock.** The meter runs the whole time — smoke locally on CPU before renting, launch detached, release the instant verification passes. 2. **Cheap checks before expensive compute.** A 1–2 batch CPU smoke (logger off) kills import/config/shape/scale bugs for ~free. (Smoke *content* → `verifying-dl-experiments`.) 3. **Trust artifacts you loaded, not log lines that claim success.** "synced/saved/done" lies under a silently-failed write; a watcher's own state is also a claim — reconcile it against the real process/artifact. 4. **Know what survives stop vs destroy.** Per platform, identify exactly which mount survives a *stop* and which survives a *terminate* — the data you need often lives on the volatile one. (The single biggest portability trap.) 5. **Storage fails on the dimension you're not watching.** Disk dies on **inodes** before bytes; the real hog hides in a symlinked cache; clean by value (keep tiny evidence, drop big scratch); monitor `df -i`, not just `df -h`. 6. **Never mutate inputs under a live run.** A running job holds its scripts in memory by byte-offset; overwriting one mid-run re-executes blocks. Version filenames. 7. **Design for retry — failure is probabilistic, transfers are flaky, mirrors are route-specific.** Make wrappers idempotent + resumable; retry the *identical* config; wrap bulk transfers in `timeout`+resume loops; a mirror/proxy speeds ONE route — validate on the same route the real transfer uses. 8. **Checkpoint-to-durable + idempotent resume is the universal spine.** File checkpoint to the platform's durable location + unconditional load-latest-on-startup is the *one* mechanism that survives an SSH drop, a Slurm walltime kill, a K8s reschedule, a spot preemption, and a Colab disconnect. The detach primitive (tmux/sbatch/Job/commit) is the swappable plug; this is the invariant. 9. **Cost and destructive actions are the user's call.** Never auto-release/terminate, never delete durable files without confirmation; if cleanup can't free space, **ask to expand the disk** rather than silently shrink the experiment. 10. **Teach the user the platform, don't just drive it.** Most users don't know a platform's non-obvious **conveniences** (one-click SSH-key registration, GPU-availability notifications, built-in panels) or its **danger clocks** (auto-release/auto-delete timers on a *stopped* box — AutoDL releases a 关机 instance after 15 days → data disk gone; a stop that keeps billing; low-balance purge). Surface them on first contact — #9 stops the agent *doing* the dangerous thing, #10 *warns the human* before the clock fires. Per-platform list → each profile's **Surface to the user** block. > **Monitoring physics (substrate for #3):** foreground Bash hard-caps at 600 s; `run_in_background` has no cap and notifies on exit; a never-exiting watcher never notifies; an unquoted `|` in a poll regex reads stdin and hangs forever. The four-layer monitoring architecture is built on these facts → `references/monitoring_patterns.md`. ## Code discipline (the wrapper & training scripts you write) Two rules govern the launch/wrapper/training code this skill has you write — corollaries of #1 and #8, not new invariants: 1. **Reuse before writing.** Take the lowest rung that already works before adding code: the base image's pre-installed stack + platform features → a framework/library utility (`torchrun` / `accelerate` / HF) → your existing `scripts/` templates → minimal new code. On a metered box a needless `pip install` also burns paid wall-clock and can break the image's ABI — Phase 1's rule (*the prebuilt image **is** the env; don't `conda create` on a rental*) is exactly this principle applied to dependencies. 2. **Floor — `minimum` bounds scope, not correctness.** Shrinking code must never drop what makes an expensive run survivable: checkpoint-to-durable + idempotent resume (#8), atomic writes, the error handling that prevents losing a long run, or seed/determinism logging. Keep one minimal self-check for non-trivial logic. ## Pick your platform profile FIRST Read the matching profile **before Phase 0** — it owns every path, proxy, credential location, billing verb, and spot rule the phases below delegate to. Each follows the same 8-field schema (`profiles/_schema.md`). > **New here? The path is:** (1) find your platform in the table below → (2) read that profile's **LAUNCH** > section (it walks rent → register SSH key → reach the box) → (3) come back and run the 6 phases from Phase 0. > Already have a box you can `ssh` into? Skip straight to Phase 0. | You're on… | Profile | Kind | Detach primitive | Meter-stop verb | |---|---|---|---|---| | AutoDL (deepest, battle-tested) | `profiles/autodl.md` | ssh-rental | tmux | 关机 (stops meter, **keeps disk** — the AutoDL exception) | | RunPod | `profiles/runpod.md` | ssh-rental | tmux | **terminate** (stop still bills 2×; destroys volume disk) | | vast.ai | `profiles/vastai.md` | ssh-rental (spot) | tmux | **destroy** (stop bills disk forever) | | Lambda | `profiles/lambda.md` | cloud-api | tmux | **terminate** (no stop state) | | Paperspace | `profiles/paperspace.md` | cloud-api | tmux | **destroy + release IP + delete storage** (shut-down stops compute only) | | 恒源云 / 矩池云 / Featurize / 揽睿星舟 | `profiles/china.md` | ssh-rental | tmux | per-platform (data disk often bills while stopped) | | Bare SSH box / Slurm / K8s / Colab-Kaggle | `profiles/generic-ssh.md` | ssh / slurm / k8s | tmux / sbatch / Job / commit | **manual** (a forgotten box bills 24/7) | > **Profile confidence:** AutoDL is battle-tested from the author's daily use; the other six profiles are > built from each platform's official docs + community reports (cited inline, `verified <month>`) and not > yet independently live-tested — lean on the Phase-0 live measurements and **re-verify any teardown/ > billing fact against current docs before betting money or data** (`references/self-improvement.md` §5). **Mental verb model** (one API across all platforms; the profile binds each verb to real commands): `up` (rent+reach) → `push` (code/data on) → `run` (detached + checkpointing) → `watch` (durable monitor) → `pull` (results off + verify) → `down` (stop the meter). ## Default workflow (6 phases) Skip phases already done. Each phase delegates substrate to the profile and **ends in a runnable check**. **Phase 0 — Environment audit.** Read the profile's STORAGE survival-matrix + region/DC-lock. Measure live: `df -h && df -i <data-mount>`, cgroup `memory.max`, `nvidia-smi`. Pre-compute the checkpoint disk budget (`ckpt_size × N + scratch`). → **verify:** `nvidia-smi` shows the expected GPU and `df -i` is not near 100%. **Phase 1 — SSH + credentials.** Set the alias/env per the profile (the prebuilt image/base IS the env — do not `conda create` on a rental). **Never rented before? the profile's LAUNCH section walks rent → register SSH key → connect.** Push secrets via **stdin, never onto a shared/durable FS** (`references/ssh_transport.md`). → **verify:** `ssh <alias> 'python -c "import torch;print(torch.cuda.is_available())"'`. **Phase 2 — Wrapper + CPU-smoke gate.** Build an idempotent `run_one`/`run_queue` from `scripts/` (parameterized from the profile's OVERRIDES; **size batch/workers to the box for a standalone run, but PIN them across cells for a fair comparison** — `references/training/throughput-profiling.md`). **Run the cheap CPU smoke locally BEFORE renting** — it kills the dumb, expensive failures (e.g. `python -m <your.train.module> --limit-batches 2 --epochs 1` — substitute your own entrypoint; this gate needs your training code plugged in). → **verify:** that smoke exits 0 on 2 batches with the logger disabled. **Phase 3 — Detached launch.** Launch via the profile's detach primitive; probe briefly (log head + alive + no traceback), then **hand back** — never a blocking foreground `sleep`. → **verify:** within 60 s, the detach session is alive and the first log line shows the expected step/epoch. **Phase 4 — Durable monitoring.** For anything over ~1–2 h, deploy the **four-layer architecture** (`references/monitoring_patterns.md`): on-box self-completion chain + session patrol loop + event sentinels + recovery handbook. **On Claude Code, fire the L2 patrol via `/loop 30m` (or `ScheduleWakeup`) running `scripts/health_patrol.sh.template`**; a host with no local recurring runner wires the on-box self-push instead (`references/monitoring_patterns.md` §7). A session-bound watcher alone dies with the session. Classify each outcome → fixed remediation; **never blind-retry**. → **verify:** the patrol reports even when nothing changed. **Phase 5 — Aggregate + verify + teardown.** Checked-sync to durable storage (gate the success line on the copy result — principle #3), then **load-and-verify each artifact** (`scripts/verify_local.py`), THEN the profile's meter-stopping action. → **verify:** `verify_local.py` reports 100% OK *before* any teardown. > **Iron Law — teardown gate:** NO `release` / `terminate` / `destroy` / file-delete until checkpoints are > **pulled to local AND verified by load**, and the user has explicitly approved the cost-affecting action. > "It looked done in the log" is not evidence (principle #3). On most platforms the meter-stopping action is > **irreversible** (deletes the disk) — confirmation matters more, not less. ## Parallel ablation fan-out For N ablation cells: one job per cell, an **isolated write path per job** (no shared mutable output), launched across instances/queues. **REQUIRED:** `superpowers:dispatching-parallel-agents` supplies the independence predicate (don't fan out onto shared state) and the mandatory post-fan-out reconciliation. FS-shared deployment pattern → `references/parallel_ablation.md`. ## Quick reference — the four facts that bite per platform Full detail in each profile; this table is the at-a-glance. | Platform | Survives **stop** | Survives **destroy** | Spot grace | China mirror needed | |---|---|---|---|---| | AutoDL | /root + data + FS | FS only | n/a | yes (`/etc/network_turbo`, hf-mirror) | | RunPod | volume disk (bills 2×) | Network Volume only | ~5 s SIGTERM→KILL | no (`hf_transfer`) | | vast.ai | disk (bills forever) | nothing | ~0 s (abrupt) | no | | Lambda | n/a (no stop) | nothing | n/a (on-demand) | no | | China (恒源云/矩池云/…) | varies; data disk bills | per-platform persistent vol | n/a | yes | | generic-SSH/Slurm/K8s | you own it | you own it | Slurm SIGTERM→KillWait (def 30 s) | only if in China | ## Common gotchas (top 8 inline — full catalog in references/) The universal ones that cost the most GPU-hours. Symptom → fix; root cause + the rest in **`references/gotchas_universal.md`** (run `grep -i '<keyword>' references/gotchas_universal.md` to jump). 1. **SSH drops on `pkill -9`** (exit 255 + "Connection reset") — normal; re-ssh to verify, don't panic. 2. **tmux holds the script in memory** — editing it mid-run re-executes blocks; version the filename. 3. **Disk-full crashes `torch.save`** (`iostream error`) — pre-budget; auto-prune `latest.pth`, keep `best`. 4. **cgroup OOM with no traceback** (bare `Killed` / exit 137) — `num_workers × big-tensor`; size workers vs `memory.max`, not CPU count. 5. **Silent sync failure** — `cp … 2>/dev/null; echo synced` lies on a full/inode-exhausted FS; gate the success line on the actual copy result. 6. **Spot preemption grace is tiny (~5 s → ~0 s on the platforms profiled here; AWS-style 2-min grace only on clouds not profiled)** — a SIGTERM-flush handler is NOT a safety net; checkpoint on a timer to durable storage, load-latest unconditionally (`references/spot-resilience.md`). 7. **"Stop" rarely stops the meter** — only `terminate`/`destroy` does, and it's irreversible (deletes the disk). Know the verb from the profile before you click, and on RunPod a stopped Pod can even restart with zero GPUs. 8. **CRLF breaks `.sh` on Linux** — author on Windows → `.gitattributes` `*.sh text eol=lf`; on-box unblock `sed -i 's/\r$//'`. ## When training itself breaks (the model, not the platform) Platform ops is only half the job — once the box is running, training breaks in its own ways. The `references/training/` layer is the debug knowledge for the run itself. Boundary: **this layer owns "make it run, fast, and not crash"; `verifying-dl-experiments` owns "is the *number* real"** — cross-link it for collapse / leakage / metric-validity. Every entry is symptom → root cause → fix with cited current docs. - `references/training/oom-memory.md` — CUDA/VRAM + host-RAM OOM and the fit-it ladder (grad-accum → bf16 → activation-checkpointing → `expandable_segments` → FSDP/ZeRO → CPU/NVMe offload → LoRA/QLoRA); OOM-at-a-specific-step (first backward / val / longest batch); the memory snapshot + visualizer. - `references/training/distributed-launch.md` — `torchrun`/`accelerate`/`deepspeed` launch + env contract, DDP/FSDP/ZeRO config, and the multi-GPU **HANGS** toolkit (one-rank-diverged, rank-conditional collective, dataloader-length mismatch). Multi-node wire → `references/multinode.md`. - `references/training/precision-stability.md` — fp16/bf16/tf32 + AMP/GradScaler, NaN/Inf hunting (`detect_anomaly`), LLM **loss spikes** + divergence (warmup, clip, init, z-loss). - `references/training/throughput-profiling.md` — GPU-bound vs data-bound vs comms-bound; dataloader knobs; `torch.compile` traps; flash-attention; `torch.profiler` / Nsight.