2 Commits

Include renames

Author	SHA1	Message	Date
bvandeusenandClaude Opus 5	7f1693a40d	fix: the ML dial offered slots the machine had no cores to feed (4295) ... Operator's 2026-09-23 log: embed_image taking 107-246s each, ~49 slots in flight by Little's law, and the daily CCIP sweep dying on its 1800s soft limit in a numpy matmul. The billiard/pool.py frame in that traceback is the soft-timeout signal handler, not a pool fault. Two causes, both mine. 1. `derived_ceiling` computed the ML lane from MEMORY ALONE. Meanwhile `embedder.py` carried `_INTRA_OP_THREADS = 4` beside a comment reading "keep N_replicas x this within the cores allotted to ML" — a constraint stated where nothing could act on it. A large-memory host offered ~49 slots, the operator took what the dial offered, and the lane asked the box for ~200 torch threads. The number moves onto the lane as `threads_per_slot`, the embedder reads it rather than restating it, and the ceiling is now the smaller of the two bounds. They fail differently on purpose: too little memory is honestly zero, because the first task would OOM the container; too few cores is merely slow, so it floors at one rather than making the lane unreachable on a small box. 2. `scheduled_ccip_auto_apply` scored one image per matmul, over every image in the library, on every daily run — ~119k products each too small to pay for its own BLAS setup. `char_maxima` does the same arithmetic in blocks bounded by elements, so its memory stays flat as either axis grows. Batching changes no arithmetic: a character's score for an image is a max over that image's figures AND that character's prototypes, and max does not care how it is grouped. Pinned against the old loop written out longhand, and against itself with the blocking forced to split every row. The UI copy said the ML ceiling came from memory; it says cores or memory, whichever runs out first. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01LVjrnpQjRgHdvq95rASoiR	2026-09-23 16:46:22 -04:00
bvandeusenandClaude Opus 4.8	666b3a2ec8	refactor(ml): DRY pass — shared sweep helpers + table-driven settings (#161) ... Consolidate duplication accrued across the ML tagging + settings backend, behavior-preserving (over-DRY guard applied — the three auto-apply sweep BODIES stay separate; only their shared inner helpers are extracted). - _sigmoid / _conflict_scores / _insert_presentation_review (heads.py): the score→prob transform (6 inlined sites), the presentation conflict signal (2 sites), and the ring-loud PresentationReview insert (2 sites, single- sourced so the mode column can't drift on the shared composite PK). - _applied_or_rejected (training_data.py): the per-tag "applied ∪ rejected" skip-set, byte-identical at 3 sweep sites (heads.py x2, tasks/ml.py ccip). - ccip sweep divergence fixes: import ccip._FIGURE_KINDS + training_data._l2norm instead of local copies that silently drift when the canonical changes. - MLSettings.load / .load_sync classmethods (mirror ImportSettings); route all 8 scalar_one singleton reads through them (the session.get None-path stays). - GET serializers for MLSettings + ImportSettings are now table-driven off the same _EDITABLE tuples PATCH writes, so a new field can't be silently absent from GET (the split that historically dropped fields). - AUTO_APPLY_THRESHOLD_MIN/MAX constant single-sources the [0.5,0.999] operating range across the service clamp + the 5 API validators. - test_ml_dry_helpers.py pins _applied_or_rejected + _sigmoid. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01NsmJSQxnNxGgtM5Yz4GAqi	2026-07-13 21:41:24 -04:00