Companion to Plan 5 Task 1A (multi-seed Argo DAG). Adds the post-run
aggregation pipeline:
- scripts/gather-multi-seed-metrics.sh: thin wrapper that pulls Argo logs
for every workflow tagged foxhunt-tag=<tag>, concatenates them into
/tmp/all-logs-<tag>.txt, then dispatches to the Python aggregator.
- scripts/aggregate-multi-seed-metrics.py: stdlib-only HEALTH_DIAG parser.
Recognises both bare and JSON-envelope-wrapped HEALTH_DIAG[<epoch>]
lines, parses every <block>[<key=val> ...] segment, and emits per-
(epoch, metric_name) mean / std / median / min / max across streams
(where each stream is one (seed, fold) training run, attributed by
pod-name prefix when present, else by epoch-rewind detection — naive
epoch=0 trigger over-segments the multi-line per-epoch HEALTH_DIAG
output, fixed by requiring epoch < last_epoch to start a new stream).
Output JSON schema matches the plan example (top-level: tag,
multi_seed, folds, warmup_end_epoch, streams_seen, aggregates;
per-entry: epoch, mean, std, median, min, max, n_samples).
- scripts/aggregate-norm-stats.py: stdlib-only merger for
norm_stats_foldN_seedM.json files. Per-fold output collapses N seeds
into mean/median/std/std_dispersion arrays consumed by the
`evaluate` step's inference normaliser.
- scripts/requirements.txt: declares numpy/scipy/matplotlib for downstream
Plan 5 T4-T5 tier-validation/plotting scripts. The aggregators
themselves are stdlib-only (statistics module).
Smoke-validated on /tmp/p4t6-cleanroom-smoke.log: detects 3 streams
(matching the 3 fold runs in that log), 90 unique metrics, n_samples=3
per (epoch, metric), schema matches plan.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
