eval-standard-cleanup

Post-run consolidation for STANDARD evals (lm_eval / evalchemy): the Delphi #6279 RL-scaling-laws math suite — MATH-500 (1 seed) + AIME24 (10-seed mean±sd) + gsm8k (strict+flex) — launched by the eval-standard-launch skill on Leonardo. The deliverable is scalar scores in a tracker: no HF trace upload, no model upload, NEVER a DB registration (see §5). For the agentic Harbor-trace + Supabase path, use eval-agentic-cleanup instead.

0. Reference files (source of truth — derive the flow from these)

Local notes dir: /Users/benjaminfeuer/Documents/experiments/active/delphi/rl-scaling-laws-6279/

• eval-standard-launch skill — the launch + delphi_eval.sbatch + the per-model output layout it produces (…/experiments/delphi-eval/<RUN_NAME>/seed{42..51}/). Read it for what's already on the cluster.
• main_sft_evals/SCORES.md — the master tracker for the 54-run main grid; its header describes the consolidation flow (per-model dirs main_sft_evals/<basename>/, partials main_sft_evals/.partial/).
• EVAL_CONVENTION.md §3.4 / §5.2-E — the per-cluster naming, the idempotent-skip seed42 dir, the exact scalar-parse method, and the "COMPLETED is NOT proof — verify numeric scores" rule.
• The earlier cold-start grid is the same flow but a different tracker (eval/SCORES.md, artifacts eval/<RUN>/) — don't cross-file; the 54-run main grid lands in main_sft_evals/.

1. Per finished job — verify a real, non-empty result (do NOT record a crash as a score)

A SLURM COMPLETED state is necessary but not sufficient: evalchemy catches engine errors, exits 0, and writes an empty results: {} JSON (silent drop). For each job:

1. Confirm terminal state via sacct -j <jobid> --format=State -n -P | head -1 (the source of truth, not log-tailing).
2. Confirm the job produced a NON-EMPTY seed42 result — the §3.4 idempotent-skip dir (…/delphi-eval/<RUN_NAME>/seed42/) must exist and its results_*.json must carry numeric scores (a crashed/timed-out run leaves an empty or missing seed42). An empty/missing seed42 → leave the row pending and note it; do not fabricate a score. (If gsm8k failed after MATH-500 wrote seed42, seed42 exists but is partial — note it; deleting seed42 + re-running is a launcher action, not a cleanup one.)

2. Consolidate — rsync results into the local per-model dir + write scalar partials

Pull only the per-task results_*.json (the examples payloads live inside the JSON, ~1-2 MB each; skip the multi-MB trace subdirs). For the main grid the destination is main_sft_evals/<basename>/:

RUN=delphi-9e19-p33m67-k0p20-lr83-a002-magpie_lr1e5-sft   # = the SFT model basename, maps 1:1 to the row
DEST=/Users/benjaminfeuer/Documents/experiments/active/delphi/rl-scaling-laws-6279/main_sft_evals/$RUN
mkdir -p "$DEST"
rsync -avz --prune-empty-dirs --include='*/' --include='results_*.json' --exclude='*' \
  Leonardo:/leonardo_work/AIFAC_5C0_290/bfeuer00/experiments/delphi-eval/$RUN/ "$DEST/"

This pulls the MATH-500 seed42 JSON, the gsm8k seed42 JSON (written by the plain lm_eval step), and the AIME24 seed42..seed51 JSONs. Write the extracted scalars to main_sft_evals/.partial/<basename>.json. Keep the rsynced JSONs in <basename>/ as the archive. Parse by loading the JSON and reading only numeric keys — never print the huge examples list.

3. Parse the scalars (the §5.2-E method — round to 1 decimal to match the table)

• MATH-500 = results["MATH500"]["accuracy"] × 100 (it's a fraction; 0.018 → 1.8). From seed42.
• AIME24 = results["AIME24"]["accuracy_avg"] × 100, taken over the 10 seed dirs (seed42..seed51); report mean ± sample-stdev of the per-seed values (×100). (e.g. 0.1±0.1.)
• gsm8k (from the lm_eval output under <basename>/seed42): exact_match,strict-match → gsm8k-S and exact_match,flexible-extract → gsm8k-F, each ×100.
• Raw = mean(MATH-500, AIME24, gsm8k-strict).
• Note any format-fail cell (empty \boxed{} / model_answer:"") — that's a real signal, not an eval bug.

The marin compiler marin:experiments/evals/evalchemy_results_compiler.py documents the exact JSON shape (results[task]…) — reuse its extraction shape rather than rolling a new parse. (verify the precise key names against the rsynced JSON on first use; evalchemy versions have drifted.)

4. Update the SCORES.md row — 🚀 eval submitted → ✅ done

Fill that row's MATH-500 | AIME24 (mean±se) | gsm8k-S | gsm8k-F | Raw cells from the partial and flip status from 🚀 eval submitted to ✅ done (keep the eval job id in its column). Edit the single row (rows are independent lines) and preserve the table format exactly. A crashed/empty-seed42 job stays pending with a note (step 1) — never invent numbers.

5. HF-upload only — NEVER DB

These are scalar scores in a tracker, not a model/trace artifact. Do NOT call manual_db_eval_push.py / touch Supabase — that is the eval-agentic-cleanup (agentic / Harbor-trace) path. Results live in this experiment's docs + (optionally) as a training_logs/-style JSON on the model's HF repo; there is no leaderboard row. (Per project_delphi_sft_hf_only_no_db.)

6. Disk / hygiene

The local artifacts are small (~1-2 MB JSONs); no large delete is needed. On the cluster, leave the delphi-eval/<RUN_NAME>/ dirs in place — they back the §3.4 idempotent skip, so a re-harvest or re-submit stays safe. No find / du / rglob on GPFS; use canonical paths.

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Launcher: eval-standard-launch. Model-publishing cleanups: rl-agentic-job-cleanup / sft-job-cleanup. Per-cluster particulars (ssh, the step-ca cert refresh for rsync, the /leonardo_work/AIFAC_5C0_290/bfeuer00/… paths) → .agents/ops/<cluster>/.