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refactor(ml): drop GPU code, cap inference threads by default (#747/#872)
GPU enablement (#872) cancelled — not worth the Pascal-specific build for a
modest CPU→GPU win on an old P4. Remove the dead GPU code (device.py, the CUDA
provider branch in tagger, the .to('cuda') path in embedder) so nothing carries
it forward.

Instead, bound CPU inference threads by default so the ml-worker is a predictable
core consumer on a SHARED node — the intended scaling model is multiple worker
replicas (each --concurrency=1, each its own cgroup limit), not one big
container. ONNX Runtime and torch otherwise size their thread pools to ALL host
cores, so each replica would grab every core and oversubscribe / starve the
co-located DB+web. Cap both to _INTRA_OP_THREADS=4 (matches the prior per-worker
cpus:4 unit): run N replicas where N×4 stays within the cores allotted to ML.

- tagger: ort.SessionOptions().intra_op_num_threads = 4 (CPUExecutionProvider).
- embedder: torch.set_num_threads(4).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-06-16 13:39:55 -04:00

FabledCurator

Self-hosted media curation — gallery, ML tagging, and subscription-driven downloading in one app. Part of the FabledSword family.

Combines what was ImageRepo (gallery, ML, importer) and GallerySubscriber (gallery-dl wrapper, subscriptions, credential capture) into a single product.

Status

Pre-v1. Not yet functional.

Quick start

For local development and testing, just:

docker compose up -d
# UI: http://localhost:8080

That uses sane dev defaults baked into docker-compose.yml and the dev override (docker-compose.override.yml, auto-merged) — local builds, DEBUG logging, exposed Postgres + Redis ports on the host. No .env required.

For a production-like deployment, override the dev defaults via shell env or a .env file (see .env.example for the variable names) and use:

docker compose -f docker-compose.yml up -d
# (skips the override so containers pull registry images)

Deployment posture

FabledCurator is designed to run inside a self-hosted homelab environment over plain HTTP. If you want TLS, terminate it at your reverse proxy. The app does not generate certificates, redirect to HTTPS, or set HSTS.

CI / Forgejo setup

The repo's workflows expect:

  • Runner label python-ci — a Forgejo runner with Python 3.14, ruff, and Node 22 pre-installed. Both ci.yml and build.yml use this label. The runner image (runner-base:python-ci) is built from CI-Runner/CI-python/ in the operator's workspace; make push from that directory builds and pushes a new image when toolchain pins change.

  • Repo secret RELEASE_TOKEN — a Forgejo PAT with the following scopes:

    • write:package + read:package — for docker push to git.fabledsword.com
    • write:release — for future release-cutting workflows
    • write:issue — for future issue-management automation

    Generate at https://git.fabledsword.com/user/settings/applications. The injected GITHUB_TOKEN cannot be used because it lacks write:package.

License

Personal project; use at your own discretion.

S
Description
Self-hosted media curation — gallery, ML tagging, and subscription-driven downloads. Part of the FabledSword family. (Merge of ImageRepo + GallerySubscriber.)
Readme 16 MiB
v26.06.04.0 Latest
2026-06-04 23:21:01 -04:00
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