24d93277de
faster_whisper loads its model into RAM at startup regardless of whether tasks are routed to Cloud Run — reducing the limit to 512M caused OOM kill on container start. Restored original limits (ffmpeg: 1G, whisper: 2G). Cloud Run URLs (FFMPEG_SERVICE_URL / WHISPER_SERVICE_URL) remain set so CPU offload is still active. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
128 lines
3.9 KiB
YAML
128 lines
3.9 KiB
YAML
# =============================================================================
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# optical-dev overrides — 4 vCPU / 15 GiB RAM server (Xeon @ 2.80 GHz)
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#
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# CPU-heavy work (ffmpeg encoding, Whisper inference) is offloaded to
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# Cloud Run HTTP services — ffmpeg-worker and whisper-worker on this server
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# become lightweight HTTP dispatchers (< 5% CPU each).
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#
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# MongoDB and Redis remain local (DB stays on server, no VPC Connector needed).
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# Celery workers stay local (broker is local Redis).
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#
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# Cloud Run HTTP services (us-central1):
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# FFMPEG_SERVICE_URL = https://ffmpeg-http-service-bcb6ipdqka-uc.a.run.app
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# WHISPER_SERVICE_URL = https://whisper-http-service-bcb6ipdqka-uc.a.run.app
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# IAM: accessible-video-worker@ and video-accessibility@ SAs already have
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# roles/run.invoker on both services — no additional IAM setup needed.
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#
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# Usage:
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# docker compose -f docker-compose.yml \
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# -f docker-compose.prod.yml \
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# -f docker-compose.optical-dev.yml \
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# --env-file .env.production up -d
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# =============================================================================
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services:
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# ── Keep on this server (MongoDB/Redis constraint — DB stays local) ─────────
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mongodb:
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deploy:
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resources:
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limits:
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memory: 1G
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cpus: '0.5'
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reservations:
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memory: 512M
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cpus: '0.25'
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redis:
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deploy:
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resources:
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limits:
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memory: 512M
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cpus: '0.25'
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reservations:
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memory: 256M
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cpus: '0.1'
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api:
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deploy:
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resources:
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limits:
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memory: 2G
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cpus: '1.0'
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reservations:
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memory: 1G
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cpus: '0.5'
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environment:
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APP_ENV: prod
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USE_CELERY_FALLBACK: "true"
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WORKER_CONCURRENCY: "2"
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FFMPEG_SERVICE_URL: "https://ffmpeg-http-service-bcb6ipdqka-uc.a.run.app"
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WHISPER_SERVICE_URL: "https://whisper-http-service-bcb6ipdqka-uc.a.run.app"
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# Full worker: handles ALL queues in fallback mode
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worker:
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deploy:
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replicas: 1
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resources:
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limits:
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memory: 2G
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cpus: '0.75'
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reservations:
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memory: 1G
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cpus: '0.25'
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environment:
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APP_ENV: prod
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WORKER_CONCURRENCY: "2"
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FFMPEG_SERVICE_URL: "https://ffmpeg-http-service-bcb6ipdqka-uc.a.run.app"
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WHISPER_SERVICE_URL: "https://whisper-http-service-bcb6ipdqka-uc.a.run.app"
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command: >
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celery -A app.tasks worker
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--loglevel=info
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--queues=default,ingest,tts,render,ffmpeg,whisper,notify,embed
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--concurrency=2
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--hostname=full-worker@%h
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# ── Pipeline workers — enabled in fallback mode ────────────────────────────
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# ffmpeg-worker: CPU-intensive encoding runs on Cloud Run (ffmpeg-http-service).
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# Memory limit kept at 1G — local ffmpeg may still run during GCS file staging.
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ffmpeg-worker:
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deploy:
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replicas: 1
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resources:
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limits:
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memory: 1G
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cpus: '0.5'
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reservations:
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memory: 256M
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cpus: '0.1'
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environment:
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FFMPEG_SERVICE_URL: "https://ffmpeg-http-service-bcb6ipdqka-uc.a.run.app"
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tts-worker:
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deploy:
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replicas: 1
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resources:
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limits:
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memory: 512M
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cpus: '0.25'
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reservations:
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memory: 128M
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cpus: '0.1'
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# whisper-worker: Whisper inference runs on Cloud Run (whisper-http-service).
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# Memory limit kept at 2G — faster_whisper loads the model into memory at startup
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# regardless of whether tasks are routed to Cloud Run.
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whisper-worker:
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deploy:
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replicas: 1
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resources:
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limits:
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memory: 2G
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cpus: '0.5'
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reservations:
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memory: 512M
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cpus: '0.25'
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environment:
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WHISPER_SERVICE_URL: "https://whisper-http-service-bcb6ipdqka-uc.a.run.app"
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