Files
mule-image/docker-compose.yml
root 80dd9d0a8b feat(auth): OIDC link by preferred_username (opt-in)
Adds OIDC_LINK_BY_USERNAME as a last-resort linking step after
(issuer, sub) and email both miss. Matches IdP preferred_username
against users.username.

Why: local accounts created before OIDC never collected an email
(no UI for it), so the email fallback cannot relink them. A new
SSO login therefore falls into JIT and creates username-1. On a
single-tenant homelab where the IdP owns the namespace, matching
by username is safe and makes first-time SSO transparent for
pre-existing users. Gated behind a flag so multi-tenant deployments
keep the stricter default.
2026-04-22 22:24:13 +02:00

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services:
frontend:
build:
context: ./frontend
dockerfile: Dockerfile
container_name: mulita-frontend
ports:
# Host port is configurable via FRONTEND_PORT in .env so multiple
# instances / other services on the same host don't collide.
- "${FRONTEND_PORT:-3000}:80"
depends_on:
- backend
networks:
- mulita-network
restart: unless-stopped
backend:
build:
context: ./backend
dockerfile: Dockerfile
container_name: mulita-backend
ports:
# Direct backend access on the host is rarely needed (the frontend
# talks to it through the nginx /api proxy on the same network),
# but it's exposed for debugging / curl. Override with BACKEND_PORT.
- "${BACKEND_PORT:-8001}:8000"
volumes:
- ./mulita.yml:/app/config/mulita.yml:ro
# The single host → container mount for your photo library. Set
# PHOTO_DIRS in .env to your library root. Mounted :rw because file
# operations (rename, move, empty discard pile) need to mutate the
# filesystem; flip to :ro for a strict read-only library and the
# write endpoints will return EROFS.
- ${PHOTO_DIRS:-./photos}:/photos:rw
- thumbs_data:/data/thumbs
- proxies_data:/data/proxies
- db_data:/data/db # retained so the docker-compose.sqlite.yml override has somewhere to put mulita.db
# Run Alembic migrations before starting uvicorn. On a fresh Postgres
# the empty 0001 baseline is a no-op stamp; create_all in init_db then
# builds the schema.
# init_db creates all tables from models (idempotent create_all),
# then Alembic runs migrations for existing installs. On fresh DBs
# create_all already built the full schema, so bootstrap.py stamps
# alembic head to skip redundant ALTER statements.
command: sh -c "python -c 'import asyncio; from app.database import init_db; asyncio.run(init_db())' && python bootstrap.py && uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload"
environment:
- DATABASE_URL=postgresql+asyncpg://mulita:mulita@db:5432/mulita
- REDIS_URL=redis://redis:6379
- CELERY_BROKER_URL=redis://redis:6379
- CELERY_RESULT_BACKEND=redis://redis:6379
- PHOTO_DIRS=/photos
- ALLOWED_ORIGINS=${ALLOWED_ORIGINS:-*}
- SECRET_KEY=${SECRET_KEY:-mulita-dev-secret-change-me}
- ACCESS_TOKEN_EXPIRE_MINUTES=${ACCESS_TOKEN_EXPIRE_MINUTES:-60}
- REFRESH_TOKEN_EXPIRE_DAYS=${REFRESH_TOKEN_EXPIRE_DAYS:-30}
# Authentik / OIDC single sign-on. Leave OIDC_ENABLED=false to
# hide the SSO button and stick with username/password. When
# enabled, set OIDC_ISSUER to the Authentik provider URL (the one
# that serves /.well-known/openid-configuration), and paste the
# client id/secret from the Authentik application. OIDC_REDIRECT_URI
# must match the one registered on the Authentik side exactly —
# e.g. https://photovault.example.com/api/v1/auth/oidc/callback.
- OIDC_ENABLED=${OIDC_ENABLED:-false}
- OIDC_ISSUER=${OIDC_ISSUER:-}
- OIDC_CLIENT_ID=${OIDC_CLIENT_ID:-}
- OIDC_CLIENT_SECRET=${OIDC_CLIENT_SECRET:-}
- OIDC_REDIRECT_URI=${OIDC_REDIRECT_URI:-}
- OIDC_SCOPES=${OIDC_SCOPES:-openid profile email}
- OIDC_PROVIDER_LABEL=${OIDC_PROVIDER_LABEL:-Authentik}
- OIDC_ALLOW_SIGNUP=${OIDC_ALLOW_SIGNUP:-true}
- OIDC_ADMIN_GROUPS=${OIDC_ADMIN_GROUPS:-}
- OIDC_LINK_BY_USERNAME=${OIDC_LINK_BY_USERNAME:-false}
- SESSION_SECRET=${SESSION_SECRET:-}
- LOG_LEVEL=${LOG_LEVEL:-INFO}
- TZ=${TZ:-UTC}
depends_on:
redis:
condition: service_started
db:
condition: service_healthy
networks:
- mulita-network
restart: unless-stopped
# ── Celery workers ─────────────────────────────────────────────────────
#
# The ingestion pipeline is split across two worker services so CPU-heavy
# vision tasks (embed / detect / OCR / faces / classify) cannot starve
# the fast IO-bound tasks (scan / thumbnails / EXIF / phash / duplicates).
#
# worker-light listens on default,high,low — IO-bound, cheap
# worker-vision listens on vision — CPU-bound, loads ONNX
#
# Both share the same image, photo volume, and model cache, so there's
# no disk duplication and model weights are loaded lazily only by
# worker-vision. Each service has its own concurrency knob; both
# workers ship their heartbeat to the same Redis broker so the
# Settings > Workers panel lists them side-by-side.
#
# Sizing defaults target a 6-core / 16 GB host:
# CELERY_LIGHT_CONCURRENCY=2 (enough for parallel thumbnail + EXIF)
# CELERY_VISION_CONCURRENCY=5 (5 × ~2GB ONNX = ~10GB RAM, 5/6 cores)
# Raise these in .env and run `docker compose up -d worker-light worker-vision`
# to scale. Keep light under ~4 and vision under your physical core
# count; more just thrashes.
worker-light:
build:
context: ./backend
dockerfile: Dockerfile
image: mule-image-worker
container_name: mulita-worker-light
command: sh -c "python -m app.services.vision.bootstrap_models && celery -A app.tasks.celery worker --loglevel=${LOG_LEVEL:-info} --concurrency=${CELERY_LIGHT_CONCURRENCY:-2} -Q default,high,low -n light@%h"
volumes:
- ./mulita.yml:/app/config/mulita.yml:ro
- ${PHOTO_DIRS:-./photos}:/photos:rw
- thumbs_data:/data/thumbs
- proxies_data:/data/proxies
- db_data:/data/db
- models_data:/data/models
environment:
- DATABASE_URL=postgresql+asyncpg://mulita:mulita@db:5432/mulita
- REDIS_URL=redis://redis:6379
- CELERY_BROKER_URL=redis://redis:6379
- CELERY_RESULT_BACKEND=redis://redis:6379
- PHOTO_DIRS=/photos
- LOG_LEVEL=${LOG_LEVEL:-INFO}
- TZ=${TZ:-UTC}
# NullPool — see app/database.py for rationale.
- MULITA_CELERY_WORKER=1
depends_on:
redis:
condition: service_started
backend:
condition: service_started
db:
condition: service_healthy
healthcheck:
test: ["CMD-SHELL", "celery -A app.tasks.celery inspect ping -d light@$$HOSTNAME 2>/dev/null | grep -q OK"]
interval: 30s
timeout: 10s
retries: 3
start_period: 120s
networks:
- mulita-network
restart: unless-stopped
# Dedicated watcher worker — runs the long-lived watch_folders task
# on its own queue so it never blocks scan/thumbnail workers.
worker-watcher:
build:
context: ./backend
dockerfile: Dockerfile
image: mule-image-worker
container_name: mulita-worker-watcher
command: sh -c "celery -A app.tasks.celery worker --loglevel=${LOG_LEVEL:-info} --concurrency=1 -Q watcher -n watcher@%h"
volumes:
- ./mulita.yml:/app/config/mulita.yml:ro
- ${PHOTO_DIRS:-./photos}:/photos:rw
- db_data:/data/db
environment:
- DATABASE_URL=postgresql+asyncpg://mulita:mulita@db:5432/mulita
- REDIS_URL=redis://redis:6379
- CELERY_BROKER_URL=redis://redis:6379
- CELERY_RESULT_BACKEND=redis://redis:6379
- PHOTO_DIRS=/photos
- LOG_LEVEL=${LOG_LEVEL:-INFO}
- TZ=${TZ:-UTC}
- MULITA_CELERY_WORKER=1
depends_on:
redis:
condition: service_started
db:
condition: service_healthy
networks:
- mulita-network
restart: unless-stopped
worker-vision:
build:
context: ./backend
dockerfile: Dockerfile
image: mule-image-worker
container_name: mulita-worker-vision
command: sh -c "python -m app.services.vision.bootstrap_models && celery -A app.tasks.celery worker --loglevel=${LOG_LEVEL:-info} --concurrency=${CELERY_VISION_CONCURRENCY:-5} -Q vision -n vision@%h"
volumes:
- ./mulita.yml:/app/config/mulita.yml:ro
- ${PHOTO_DIRS:-./photos}:/photos:rw
- thumbs_data:/data/thumbs
- proxies_data:/data/proxies
- db_data:/data/db
- models_data:/data/models
environment:
- DATABASE_URL=postgresql+asyncpg://mulita:mulita@db:5432/mulita
- REDIS_URL=redis://redis:6379
- CELERY_BROKER_URL=redis://redis:6379
- CELERY_RESULT_BACKEND=redis://redis:6379
- PHOTO_DIRS=/photos
- LOG_LEVEL=${LOG_LEVEL:-INFO}
- TZ=${TZ:-UTC}
- MULITA_CELERY_WORKER=1
# ONNX Runtime execution providers. Set to "auto" to auto-detect
# GPU (CUDA > ROCm > OpenVINO > CPU), or explicitly:
# "CUDAExecutionProvider,CPUExecutionProvider"
# "ROCMExecutionProvider,CPUExecutionProvider"
# Default: CPU only. To enable GPU, also uncomment the deploy
# section below and install nvidia-container-toolkit on the host.
- VISION_EXECUTION_PROVIDERS=${VISION_EXECUTION_PROVIDERS:-CPUExecutionProvider}
# Pin each ONNX session to one intra-op thread so N prefork children
# × default-all-cores doesn't oversubscribe the box. With
# concurrency=5 and OMP=1, vision peaks at 5 busy cores, leaving
# one for worker-light + system. These env vars cover the three
# threading runtimes ONNX Runtime might pick up on first use.
- OMP_NUM_THREADS=1
- OPENBLAS_NUM_THREADS=1
- MKL_NUM_THREADS=1
# Uncomment for NVIDIA GPU passthrough:
# deploy:
# resources:
# reservations:
# devices:
# - driver: nvidia
# count: all
# capabilities: [gpu]
healthcheck:
test: ["CMD-SHELL", "celery -A app.tasks.celery inspect ping -d vision@$$HOSTNAME 2>/dev/null | grep -q OK"]
interval: 30s
timeout: 10s
retries: 3
start_period: 300s
depends_on:
redis:
condition: service_started
backend:
condition: service_started
db:
condition: service_healthy
networks:
- mulita-network
restart: unless-stopped
db:
image: pgvector/pgvector:pg16
container_name: mulita-db
environment:
POSTGRES_USER: mulita
POSTGRES_PASSWORD: mulita
POSTGRES_DB: mulita
volumes:
- pg_data:/var/lib/postgresql/data
networks:
- mulita-network
restart: unless-stopped
healthcheck:
test: ["CMD-SHELL", "pg_isready -U mulita -d mulita"]
interval: 5s
timeout: 5s
retries: 10
redis:
image: redis:7-alpine
container_name: mulita-redis
# Host port exposed only for local debugging; the backend / worker
# reach Redis via the internal mulita-network on its container name.
ports:
- "${REDIS_PORT:-6379}:6379"
volumes:
- redis_data:/data
networks:
- mulita-network
restart: unless-stopped
command: redis-server --appendonly yes
healthcheck:
test: ["CMD", "redis-cli", "ping"]
interval: 10s
timeout: 5s
retries: 5
networks:
mulita-network:
driver: bridge
volumes:
thumbs_data:
proxies_data:
db_data:
redis_data:
pg_data:
models_data: