run-ferries

name	run-ferries
description	Launch, monitor, and seal Marin canary and daily ferry runs.

Skill: Ferries (Canary + Daily)

Overview

Two ferry lanes:

• canary: fast, low-cost always-on health check
• daily: higher-scale integration run with bounded changes

Both keep core data assumptions aligned and share the same monitoring/triage discipline.

Ferry Lanes

Templates:

• experiments/ferries/canary_ferry.py (MoE canary, TPU and GPU via CANARY_ACCELERATOR)
• experiments/ferries/daily.py

Intent:

• canary: catch infra/pretraining regressions early with a stable Grug MoE config (one TPU, one GPU)
• daily: exercise a larger run envelope and test small, explicit changes

Shared baseline:

• data: shared nemotron_mix baseline
• default cluster: us-central1 (zone us-central1-a)
• run log: docs/experiments/daily-ferry-log.md

Daily baseline defaults:

• model size: Llama ~150M (llama_150m)
• sequence length: 4096
• train batch size: 512
• FLOP target: ~1e19 (overrideable via env)

Inputs Before Proposing (Daily Only)

Canary runs normally do not require a proposal cycle or PR. For daily, collect:

1. Last ferry references: issue URL, PR/commit URL, W&B run URL and Iris job ID
2. Human objective for this interval: standard integration pass, or explicit regression investigation
3. Interval boundary: use "since last ferry run", not fixed wall-clock day boundaries

If objective is ambiguous, ask before editing.

Operating Policy

General

• Hard launch gate: get explicit requester approval before launching any ferry job. Only exception: the requester explicitly says to launch without asking.
• Follow the babysit-job skill until the run reaches a terminal state (SUCCEEDED/FAILED/STOPPED); do not stop early. Full ferry monitoring often takes 4-5 hours.
• Never restart/recreate/mutate cluster without explicit human consent in-thread. Keep cluster mutation guardrails aligned with babysit-job, including the debug exception path.
• Use major-event updates (not spam): launch, first eval, major incident, terminal state.
• Seal each completed daily run with a pushed git tag pointing to the exact launch commit.
• Canonical run-closure PR labels: ferry, ferry-daily, ferry-log-only, ferry-sealed.
• Canonical seal-tag format (daily): ferry/daily/YYYYMMDD/<run_slug>

Canary

• Keep canary stable; only change it for explicit reliability fixes, and only when diagnosing/fixing a concrete failure mode.
• Canary launches usually do not require a PR if the script/config is unchanged.
• If canary fails, treat as urgent infrastructure/training-health triage.
• Canary is run-only by default (W&B + issue updates); no sealing tag or run-closure PR in the normal path.

Daily

• Use daily for bounded evolution, usually 1-2 knobs.
• If daily fails, debug with one bounded fix attempt, then escalate.
• Run-closure PR scope is log-only: update docs/experiments/daily-ferry-log.md, keep detailed debug/run narrative in the issue.

Workflows

Daily lane (proposal + run)

1) Build context since last ferry

Check the latest entries in docs/experiments/daily-ferry-log.md.

LAST_FERRY_SHA=<last_ferry_commit_sha>
LAST_FERRY_DATE=<YYYY-MM-DD>

git log --oneline "${LAST_FERRY_SHA}..HEAD" -- experiments/ lib/ scripts/

gh issue list \
  --label experiment \
  --search "updated:>=${LAST_FERRY_DATE}" \
  --limit 100

Treat GitHub-tagged ferry PRs/issues as source of truth. Use "since last ferry run" rather than fixed wall-clock boundaries.

2) Edit experiments/ferries/daily.py

• Keep edits bounded (typically 1-2 knobs); propose at least one intentional modification each interval.
• If no obvious change emerges from recent commits/issues/ferry history, pick a low-risk tweak (e.g. data-mix adjustment or hyperparameter change) that may improve loss at the same FLOPs budget.
• Pattern-match from the previous daily ferry; avoid high-churn rewrites.
• Update run naming for this interval (e.g. via FERRY_DATE in launch env: daily-125m-YYYY-MM-DD style).

3) Record proposal in issue and push launch commit

In the run issue, record:

• last ferry links (issue + commit + W&B/job link),
• exact config delta and rationale,
• risk level (low/medium/high),
• relaunch fallback note,
• why this run is not literally identical to the previous daily run,
• launch checklist (explicit requester approval or explicit waiver + monitoring started).

Then push the launch commit (no proposal PR by default).

4) Launch

Confirm requester approval in-thread unless they already gave explicit "launch without asking" permission.

uv run iris --cluster=marin job run --no-wait --cpu=1 --memory=2G --extra=cpu \
  -- python -m experiments.ferries.daily

After launch, capture and post to the issue:

• Iris job id (printed by iris job run, form /<user>/iris-run-job-YYYYMMDD-HHMMSS)
• cluster
• launch timestamp
• W&B link(s) when available

Optional deterministic daily rerun name:

uv run iris --cluster=marin job run --no-wait --cpu=1 --memory=2G --extra=cpu \
  -e FERRY_DATE "$(date +%Y%m%d-%H%M%S)-daily-ferry" \
  -- python -m experiments.ferries.daily

5) Monitor to terminal state

Follow the babysit-job skill with job_id, cluster, experiment=<ferry script path>.

• Keep the monitoring loop active until terminal status; ferry runs commonly take 4-5 hours.
• Follow monitoring-loop restart policy for recoverable failures.
• Escalate non-trivial failures to humans.

6) Close the loop

Post in the ferry issue: final status, key metrics/regressions, Iris job ID and W&B link(s), recommendation for next ferry. Optional: post a manual Discord update for major run state changes.

For daily-log metric fields, extract canonical final keys with:

uv run python scripts/ferries/daily_analysis.py \
  --run <wandb_run_url_or_path> \
  --format markdown

Required terminal issue comment template:

Final status: <SUCCEEDED|FAILED|STOPPED>
Iris job id: <job_id>
W&B link: <url>
Final eval summary: <short summary + key metrics>
Experiment link: <experiment JSON/browser link>
Recommendation / victory decision: <next action>

7) Seal and open log-only PR

• Create and push a sealing tag for the exact launch commit (the commit containing the experiments/ferries/daily.py used for the run).
• Open a PR that updates only docs/experiments/daily-ferry-log.md, following .agents/skills/commit/SKILL.md for description format.
• Keep all detailed launch/retry/debug narrative in the run issue, not in the PR.
• Apply canonical labels: ferry, ferry-daily, ferry-log-only, ferry-sealed.

Canary lane (steady-state run)

Default mode: launch the existing canary script as-is and monitor. Do not run the daily proposal/PR loop unless intentionally changing canary. Even for unchanged runs, ask the requester before launch unless they explicitly waived that requirement.

Launch (TPU):

uv run iris --config=lib/iris/config/marin.yaml \
  job run --memory=16G --disk=16G --cpu=1 --extra=tpu \
  -- python -m experiments.ferries.canary_ferry

Launch (GPU / CoreWeave):

uv run iris --config=lib/iris/config/coreweave.yaml \
  job run --memory=16G --disk=16G --cpu=1 --extra=cpu \
  -e MARIN_PREFIX s3://marin-na/marin \
  -e CANARY_ACCELERATOR gpu \
  -- python -m experiments.ferries.canary_ferry

If canary fails: triage and identify root cause, only then open a focused PR if a canary script/config change is necessary, relaunch and monitor to terminal state.

Canary profiling triage

• The full profile_summary.json is in the workflow logs (default params: --warmup-steps 5, --breakdown-mode exclusive_per_track, --hot-op-limit 25). The step summary has pointers to the raw trace artifact and W&B run.
• The log summary is ephemeral and not published. To re-analyze with different parameters, fetch the raw trace via --run-target — see .agents/skills/profile-training/.
• If the canary failed early, the profile may only cover warmup steps — check step_time.all_steps.count before drawing conclusions from steady-state stats.
• exclusive_per_track (the default) can hide device stalls that overlap across tracks. Use exclusive_global when investigating stall-heavy profiles.

Promotion Rule (Daily)

If a daily variant is clearly better holistically, promote it as the new default daily recipe/template.

Promotion signals:

• eval losses are broadly better,
• LM eval soft metrics improve in aggregate,
• no reliability regressions.

When promoting: open a follow-up PR updating experiments/ferries/daily.py and this skill; include a concise before/after metrics table.

Validation Checklist

• Diff is intentional and bounded for the selected lane.
• If daily was edited, launch commit is pushed and referenced in the run issue.
• Run-closure PR only updates docs/experiments/daily-ferry-log.md.
• Ferry issue has updated launch metadata.
• Monitoring loop ran until terminal state.

See Also

• docs/experiments/daily-ferry-log.md
• .agents/skills/babysit-job/SKILL.md
• .agents/projects/ferry_framework.md
• .agents/skills/run-research/