Milestone 422 step 6. Dockerfile.ml is gone; the main image carries torch,
torchvision, transformers, onnxruntime and opencv, and serves every lane.
WHY IT HAD TO MERGE: step 5 runs every lane in one process tree, so a second
image would mean the `ml` lane could never be enabled from the UI — there
would be no worker in that container to enable. The switch needs something to
switch.
THE MODEL NO LONGER DOWNLOADS AT BOOT. `entrypoint.sh`'s ml-worker role ran
download_models before celery started, so every boot of that role reached
HuggingFace for ~3.5GB — a startup dependency on a third party for a feature
the operator may never use. Rule 164 permits a runtime fetch only for
something "optional and clearly off", so the fetch is now a TASK, enqueued
the moment the lane is ENABLED.
Being a task is what makes it visible: it gets a TaskRun row, so the download
shows in Activity with a duration and a status, and a failure is something an
operator can see and retry rather than a container that quietly never became
useful. Idempotent, so re-enabling a provisioned lane costs one no-op.
Enqueued only when the lane actually came ON (`enabled is True`, not the
resolved value) so re-saving slots does not re-fetch, and only when the
consumer change landed — a task queued onto a queue nothing consumes would
sit pending with no explanation.
`fabledcurator-ml` KEEPS PUBLISHING, from the merged Dockerfile. The
operator's Swarm stack references that name and lives outside this repo;
dropping it would not break their deploy, it would freeze it silently at the
last publish — the exact failure class this milestone keeps finding. Retiring
the NAME is its own task, gated on that stack moving. Same two-phase shape
#406 used for pixiv.
THREE LIVE BREAKAGES from deleting the file, found by grepping for it rather
than assuming the build was the only consumer:
- `docker-compose.override.yml` built the ml service from it (contributor
path would have failed at `docker compose build`).
- `tests/test_artifact_paths.py` pins the ml path set.
- `scripts/artifacts.sh` ML_PATHS named it. A path set naming a deleted file
silently stops contributing to the derived revision — which the reuse check
and the version string both read. That is #3202's recorded shape.
The `--with-ml` flag is gone from the generator and the healthcheck rather
than left defaulting to true. One image carries every lane now, so a flag
that can only be passed one way is a branch pretending to be a choice.
The advisory shipped in ecbd325 is what makes this honest to an adopter: the
lane says it is optional, names the model, and gives its download and
per-slot RAM before the switch is thrown.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LVjrnpQjRgHdvq95rASoiR
Slice 1 of milestone #114 (tagging v2). Proves the frozen-embedding + trained-
head spine on the operator's own data, reusing the SigLIP embeddings already
stored on image_record — no re-embedding, no GPU.
Per concept: train a logistic-regression HEAD (positives + negatives = explicit
rejections + sampled unlabeled) vs the old single-CENTROID baseline; report
cross-validated precision/recall/AP for both, a LEARNING CURVE (AP/F1 as tagged
positives grow 10→30→100→300), and example image ids (head-would-suggest /
head-doubts-positive) to eyeball.
Persisted so the report SURVIVES navigation (operator-flagged): the run + full
report live in a new tag_eval_run row (mirrors library_audit_run); the admin
card will rehydrate from GET on mount, not transient state.
- models.TagEvalRun + migration 0056; runs on the ml queue (only worker with
numpy/sklearn) — numpy/sklearn lazy-imported so the API can still enqueue.
- services/ml/tag_eval (compute + start helper, one-running guard), tasks.ml
.tag_eval_run, api/tag-eval (POST create, GET history light / detail w/ report).
- recover_stalled_tag_eval_runs sweep + retention (keep last 20) + 5-min beat
(rule 89). scikit-learn added to requirements-ml.
- tests: param normalization + the rehydrate read-path + create/conflict.
Frontend admin card (trigger + render persisted report) follows next.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Pins runtime and ML deps separately so the regular web image stays lean.
Configures ruff for py312 with bugbear, async, and pyupgrade lints enabled.
psycopg sync driver included up-front for alembic.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
bvandeusenandClaude Opus 5