feat: SQL=postgres, nginx+certbot, MQTT split, ML stacks, gitea HTTPS-only, gemaal1 site

Round-2 changes locking in scaffold-phase decisions and adding ML/notebook stacks.

Locked decisions
- sql: postgres 16-alpine (was TBD); init.d/ mount for per-app DB provisioning
- nginx-proxy: stock nginx + certbot sidecar (was nginx:alpine TODO).
  Chose stock over nginxproxy/nginx-proxy because stream{} is required for
  MQTT-TLS reverse-proxy on tcp/8883 to rabbitmq:1883.
- gitea: HTTPS-only (DISABLE_SSH=true). No SSH port published.

MQTT split
- Remove stacks/mqtt placeholder.
- Add stacks/rabbitmq — general-purpose broker (AMQP + MQTT plugin),
  used at both cloud and edge. External MQTT clients reach cloud broker
  via nginx stream-proxy on 8883.
- Add stacks/mosquitto — reserved for the FROST (SensorThings) stack
  only. Cloud-only. Internal to its own stack; no external ingress.

ML / notebooks (cloud-only)
- stacks/mlflow — experiment tracking + model registry. Postgres backend
  on sql stack; local volume for artifacts (S3/MinIO is a TODO).
- stacks/jupyterhub — multi-user notebook server. DockerSpawner via
  mounted docker.sock; users spawn into cloud-app network so they can
  reach mlflow, influxdb (via grafana), rabbitmq.

Sites
- sites/gemaal1 — first edge deployment scaffold. Site-local override
  template for binding nginx to PLANT_LAN_IP.

Docs
- README + docs/architecture.md updated: stacks table now lists 15 stacks,
  ingress + attachment tables reflect mlflow/jupyterhub, TLS strategy
  section locked, MQTT-split section added, Gitea HTTPS-only noted.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

stacks/jupyterhub/.env.example

@@ -0,0 +1,5 @@
+# Image spawned per user (override to a custom EVOLV image with mlflow client preinstalled)
+JUPYTER_NOTEBOOK_IMAGE=jupyter/datascience-notebook:latest
+
+# Admin users (comma-separated, consumed by jupyterhub_config.py)
+JUPYTERHUB_ADMIN_USERS=

stacks/jupyterhub/README.md

@@ -0,0 +1,16 @@
+# jupyterhub
+
+Multi-user JupyterHub. Each authenticated user gets their own notebook container via DockerSpawner. **Cloud-only.**
+
+- **Networks**: `app` (UI proxied at `/jupyter` or subdomain) + `mgmt` (Docker socket so JupyterHub can spawn user containers)
+- **Spawned user containers** land on the `cloud-app` network so they can reach mlflow, influxdb (via grafana proxy), rabbitmq
+- **Config**: `config/jupyterhub_config.py` — DockerSpawner setup, authenticator, admin list, resource limits
+
+## TODO
+
+- DockerSpawner config (image, network, user volumes, idle culling)
+- Keycloak OAuth via `oauthenticator.generic.GenericOAuthenticator`
+- Build a project-specific notebook image with EVOLV libs + mlflow client + InfluxDB client preinstalled
+- Per-user persistent volume mounted at `/home/jovyan/work`
+- CPU / memory limits per user container
+- Cull idle servers (`c.JupyterHub.services` cull-idle pattern)

stacks/jupyterhub/compose.yml

@@ -0,0 +1,25 @@
+# jupyterhub — multi-user notebook server (cloud only)
+# Networks: app (UI proxied by nginx) + mgmt (Docker socket for DockerSpawner)
+
+services:
+  jupyterhub:
+    image: jupyterhub/jupyterhub:5
+    restart: unless-stopped
+    networks: [app, mgmt]
+    volumes:
+      - jupyterhub-data:/srv/jupyterhub
+      - /var/run/docker.sock:/var/run/docker.sock
+      - ./config/jupyterhub_config.py:/srv/jupyterhub/jupyterhub_config.py:ro
+    environment:
+      TZ: ${TZ:-Europe/Amsterdam}
+      DOCKER_NOTEBOOK_IMAGE: ${JUPYTER_NOTEBOOK_IMAGE:-jupyter/datascience-notebook:latest}
+      DOCKER_NETWORK_NAME: cloud-app
+    # TODO: DockerSpawner config in jupyterhub_config.py; Keycloak OAuthAuthenticator;
+    #       preinstalled libraries; per-user persistent volumes; CPU/memory limits
+
+networks:
+  app:
+  mgmt:
+
+volumes:
+  jupyterhub-data: