/experiment-queue

name: experiment-queue
description: SSH job queue for multi-seed/multi-config ML experiments with OOM-aware retry, stale-screen cleanup, and wave-transition race prevention. Use when user says "batch experiments", "队列实验", "run grid", "multi-seed sweep", "auto-chain experiments", or when /run-experiment is insufficient for 10+ jobs that need orchestration.
argument-hint: "[manifest-or-grid-spec]"
allowed-tools: Bash(*), Read, Grep, Glob, Edit, Write, Skill(run-experiment), Skill(monitor-experiment)

Experiment Queue

> ⏱ **External cadence: visibility only.** This skill already runs its own > detached server-side scheduler (60s poll + `depends_on` + wave transitions). > Use its status output for overnight visibility (N done / N running / N > pending); do **not** wrap it in a second `/loop` / `CronCreate` poll — that > duplicates the scheduler on an uncoordinated clock and races the > wave-transition logic it was built to prevent. See > [`shared-references/external-cadence.md`](../shared-references/external-cadence.md) > ("don't duplicate an existing scheduler").

Orchestrate large batches of ML experiments on SSH remote GPU servers with proper state tracking, OOM retry, stale cleanup, and wave transitions.

When to Use This Skill

Use when `/run-experiment` is insufficient:

• **≥10 jobs** that need batching across GPUs
• **Multi-seed sweeps** (e.g., 21 seeds × 12 cells)
• **Wave transitions** (run wave 1, wait, run wave 2, wait, run wave 3...)
• **Teacher+student chains** (train teacher then distill; auto-trigger student after teacher done)
• **OOM-prone configs** where you need to retry with different GPU or wait
• **Mixed seed grids** where failed cells need re-running

Do NOT use for:

• Single ad-hoc experiment (use `/run-experiment`)
• Modal/Vast.ai deployments (those have their own orchestration)
• Experiments that need manual inspection between runs

Why This Exists

Based on session audit (2026-04-16), the major wall-clock sinks in multi-seed grid experiments are:

1. **Stale screens** — python finishes, wandb uploads, screen hangs, next wave blocked 2. **OOM on shared GPU** — previous job's memory not yet released 3. **Wave race** — new wave launches before previous wave fully settles 4. **Missing checkpoints** — student launches before teacher saved 5. **Parser duplication** — rewriting multi-seed analysis python every batch

All of these are pure engineering friction that can be orchestrated.

Core Concepts

> **Environment contract**: queue jobs assume the target env is already built > and validated per `../shared-references/compute-env-contract.md` (spec-hash > ledger + kernel witness). A wave of jobs dying at import time = the env > contract was skipped, not a queue bug; check the provider's > `.aris/compute/<provider>.md` ledger before re-queueing.

Job Manifest

A manifest lists jobs with explicit state:

project: my_grid_experiment
cwd: /home/user/your_project
conda: my_env
# Optional: override conda hook path if conda is not at a standard location.
# Can be a bare path (wrapped automatically) or a full `eval "$(... shell.bash hook)"` string.
# Falls back to auto-detect of ~/anaconda3, ~/miniconda3, /opt/anaconda3, etc.,
# or the ARIS_CONDA_HOOK environment variable.
# conda_hook: /custom/path/to/conda
ssh: gpu-server
default_cmd: >
  python run_distill.py --backbone softmax --lam 0.5
  --K 500 --L 96 --W 16 --n_steps 30000 --batch_size 128 --lr 1e-4

preconditions:
  - type: checkpoint_exists
    path: checkpoints/transformer/teacher_L96_K500_N{N}.pt

gpus: [0, 1, 2, 3, 4, 5, 6, 7]
max_parallel: 8
gpu_free_threshold_mib: 500  # optional, default 500; raise for shared servers, lower for tight packing
oom_retry:
  delay: 120
  max_attempts: 3

jobs:
  - id: s200_N64_n50K
    args: {seed: 200, n_hidden: 64, n_train_subset: 50000, subset_seed: 2024}
  - id: s200_N128_n50K
    args: {seed: 200, n_hidden: 128, n_train_subset: 50000, subset_seed: 2024}
  # ... 14 more

Job State Machine

pending → running → completed
                 ↘ failed_oom → pending (after delay) [retry up to N]
                 ↘ failed_other → stuck (needs manual inspection)
stale screen (process gone, screen lingering) → failed_other → stuck

> **Operator note on `stuck` (the agent's move, not the queue's):** the queue > deterministically parks `failed_other` jobs as `stuck` — that part is code and > unchanged. Before handing a `stuck` batch to the human, the OPERATING AGENT > should check: if the same failure repeats across jobs, try ONE clean > reimplement of the **agent-generated wrapper/attempt script only** — never > user/project source (`run_*.py` you didn't write), the manifest, queue state, > logs, or results (see `shared-references/external-cadence.md` § *Let a broken > attempt restart, not just patch*). Reserve the human handoff for > contract/environment doubts, not merely broken attempt code.

Wave Orchestration

A "wave" is a batch of jobs that fit available GPUs. Next wave only starts when: 1. All current-wave python processes have exited 2. No stale screens remain for current-wave tags 3. GPU memory has dropped below threshold (≤500 MiB) 4. Precondition checks pass for next-wave jobs

Workflow

Step 1: Parse Manifest / Build from Grid

Input can be:

• **YAML manifest** (explicit job list, recommended for complex cases)
• **Grid spec** (Cartesian product of param values, e.g., `N=[64,128,256] × n=[50K,150K,500K,652K]`)
• **Natural language description** (Claude parses into manifest)

Bind the run identifiers once so every later step (manifest save, scp, launch, monitor, resume) refers to the same paths. Set these as local shell variables before generating the manifest:

# REPLACE the placeholder path before running, or pre-export PROJECT_DIR:
PROJECT_DIR="${PROJECT_DIR:?set PROJECT_DIR to the local project root}"
RUN_TS=$(date -u +%Y%m%dT%H%M%SZ)             # one timestamp per run, reused everywhere
LOCAL_RUN_DIR="$PROJECT