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docs(docker): Hub pages for aio + ml, and the image table in the README

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picpeak/ml has an empty Hub overview and picpeak/aio has none at all,
while backend and frontend carry hand-written ones — so the two newest
images are the two with nothing on their registry page.

Adds .github/dockerhub/{aio,ml}.md as the source of those pages and a
dockerhub-descriptions job that pushes them on every main merge, so the
page cannot drift from the release it describes. backend/frontend stay
hand-maintained for now: capturing their current Hub text into files is
a prerequisite, not a side effect of this change.

README gains a registry table for all four images (both registries share
digests and tags), the org-move callout lists the full set, and the
feature list finally mentions People in this gallery, which shipped in
#1074 without a README line.
This commit is contained in:
Luca
2026-08-20 08:24:19 +02:00
parent 2df455784c
commit 899c9b3407
5 changed files with 170 additions and 3 deletions
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.github/dockerhub/aio.md
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# picpeak — All-in-one
**picpeak** is an open-source, self-hosted **photo-sharing platform for photographers**, with an optional CRM / accounting suite. This image is the **all-in-one** build: the backend, the built web UI and SQLite in **one container, one process** — no compose file, no separate database, no reverse proxy to wire up.
- 📦 **Source, docs & issues:** https://github.com/PicPeak/picpeak
- 🧩 **Multi-container images:** [`picpeak/backend`](https://hub.docker.com/r/picpeak/backend) + [`picpeak/frontend`](https://hub.docker.com/r/picpeak/frontend)
## Supported tags
- `latest` / `stable` — latest stable release
- `x.y.z` — a pinned release (**recommended for production**)
- `beta` / `main` — latest build from `main` (may be unstable)
- **Architectures:** `linux/amd64`, `linux/arm64` (x86 and ARM NAS)
## Quick start
docker run -d --name picpeak -p 3000:3000 \
-v picpeak:/data \
-e JWT_SECRET="$(openssl rand -base64 48)" \
picpeak/aio:stable
Then open **http://localhost:3000/admin** and complete the setup wizard. Read the one-time setup token with:
docker exec picpeak cat /data/db/SETUP_TOKEN
> 🔗 Share links need to know your address. The image defaults `FRONTEND_URL` to `http://localhost:3000`; pass `-e FRONTEND_URL=https://photos.example.com` (or set the site URL in Settings) before you send a gallery to a client.
## Ports & volumes
- Container port **3000** (HTTP; put your own TLS terminator in front for public use).
- **One volume: `/data`** — back it up and you have backed up the install.
- `/data/db` — `picpeak.db` and `SETUP_TOKEN`
- `/data/storage` — originals, thumbnails, archives
- `/data/logs`, `/data/backup`
## External Postgres
SQLite is this image's default, not its only option. Point it at an existing database exactly like the backend image:
-e DATABASE_CLIENT=pg -e DB_HOST=… -e DB_USER=… -e DB_PASSWORD=…
## How it differs from the compose stack
- **SQLite takes one writer at a time** — right for a home server, a NAS or a single studio; the compose stack with PostgreSQL is what scales.
- **No Redis** — background jobs run in-process.
- **Face recognition is unavailable** here. It needs the separate [`picpeak/ml`](https://hub.docker.com/r/picpeak/ml) sidecar, and a second image-processing pipeline competing with thumbnailing for one container's CPU would just make the install slow. Run the multi-container deployment for that feature.
You can move to the full stack later without reinstalling: take a `.picpeak` backup and restore it there.
## Docs
Volume layout, the external-Postgres variant, TLS, updates and the limits: **https://docs.picpeak.app/deployment/single-container**
.github/dockerhub/ml.md
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# picpeak — ML sidecar (face detection)
**picpeak** is an open-source, self-hosted **photo-sharing platform for photographers**. This image is the **optional face-detection sidecar**: it detects faces in one image and returns a bounding box, five landmarks, quality signals and a 512-d embedding per face.
**Nothing else.** No database, no volumes, no state, no egress, no model download at runtime. Clustering, person identity, thresholds and every privacy decision live in the picpeak backend, where the data already is — this service forgets each image the moment it answers.
If you don't run this container, the feature does not exist.
- 📦 **Source, docs & issues:** https://github.com/PicPeak/picpeak
- 🧩 **Runs with:** [`picpeak/backend`](https://hub.docker.com/r/picpeak/backend) + [`picpeak/frontend`](https://hub.docker.com/r/picpeak/frontend)
## Supported tags
- `latest` / `stable` — latest stable release
- `x.y.z` — a pinned release (**recommended for production** — keep it on the **same** tag as the backend)
- `beta` / `main` — latest build from `main` (may be unstable)
- **Architectures:** `linux/amd64`, `linux/arm64`
> The sidecar's API contract is versioned with the backend that calls it, so `PICPEAK_CHANNEL` resolves the same string across all picpeak images.
## Turning it on
The maintained compose file already contains this service behind a profile — you do not write it by hand:
docker compose --profile faces up -d
Then two deliberate actions in the app, neither of which is installing this container:
1. Enable the **`faces`** feature flag in admin settings.
2. Enable **"Detect people in this gallery"** per event.
**Nothing in the backend touches this service while the flag is off**, so an install without this container never attempts a connection.
## Configuration
| | |
|---|---|
| `FACE_ML_TOKEN` | **Required.** The container **refuses to start** without it, so an accidentally published port is never a free face-detection API. Must match the backend's `FACE_ML_TOKEN`. |
| `FACE_ORT_THREADS` | ONNX Runtime threads (default `1`). |
Port **8000**, no volumes, no published ports needed — the backend reaches it on the compose network. `FACE_ML_URL` defaults to `http://picpeak-ml:8000` (the compose service name), so the standard deployment needs no URL configuration.
## API
All endpoints except `/health` require the `X-Face-ML-Token` header.
| | |
|---|---|
| `GET /health` | `{"status": "ok"}` — unauthenticated, used by the healthcheck |
| `GET /info` | `{detector, embedder, model_version, dim}` |
| `POST /faces` | multipart `image` → `{model_version, faces: [...]}` |
## Models
YuNet (detection) + FaceNet-512 (embedding), **both MIT**, baked into the image and verified by SHA-256 at build time — never downloaded at runtime, so airgapped installs work and a model cannot change under a running deployment. See [`ml/LICENSES.md`](https://github.com/PicPeak/picpeak/blob/main/ml/LICENSES.md) for why these and not InsightFace's non-commercial weights.
## Not available on the all-in-one image
[`picpeak/aio`](https://hub.docker.com/r/picpeak/aio) sets `PICPEAK_SINGLE_CONTAINER=true` and the backend refuses to enable face recognition there — a second image-processing pipeline competing with thumbnailing for one small container's CPU would not fail loudly, it would just make the install slow. Run the multi-container deployment for this feature.
## Docs
**https://docs.picpeak.app** · sidecar internals, model conversion and the alignment/threshold contract: [`ml/README.md`](https://github.com/PicPeak/picpeak/blob/main/ml/README.md)