bvandeusen
db7e1f2b59
Step 1 of GPU enablement (code only — CPU-safe, CI-green; the CUDA image is a separate step pending the host driver version). - New services/ml/device.py: FC_ML_DEVICE (auto|cuda|cpu) intent + VRAM knobs (FC_ML_ONNX_GPU_MEM_GB, FC_ML_TORCH_MEM_FRACTION). Per-worker-host bootstrap → env, not a DB setting (the GPU host runs CUDA, others CPU). - tagger: use CUDAExecutionProvider (with gpu_mem_limit) when requested AND the provider is actually present (onnxruntime-gpu), else CPUExecutionProvider. Logs the active providers. - embedder: move model + inputs to cuda when requested AND torch.cuda is available; cap torch's VRAM share; .detach().cpu() before numpy. fp32 kept so GPU embeddings stay in the same space as existing CPU ones. Both AND the env intent with the framework's real availability, so on CPU (CI / CPU onnxruntime / no GPU) they fall back cleanly — behavior unchanged. The 8GB P4 is shared by both frameworks, hence the conservative default caps. Tests: device env parsing. (tagger/embedder GPU paths are operator-verified on the GPU host — models aren't in CI.) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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. Bothci.ymlandbuild.ymluse this label. The runner image (runner-base:python-ci) is built fromCI-Runner/CI-python/in the operator's workspace;make pushfrom 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— fordocker pushtogit.fabledsword.comwrite:release— for future release-cutting workflowswrite:issue— for future issue-management automation
Generate at https://git.fabledsword.com/user/settings/applications. The injected
GITHUB_TOKENcannot be used because it lackswrite:package.
License
Personal project; use at your own discretion.