build: local stack, container images and the end-to-end smoke script
docker-compose brings up PostgreSQL 16 with pgvector and Redis 7 on non-standard host ports so it never collides with an existing local database. scripts/smoke.sh is the proof: compose, migrate, boot the API, issue a token, call the real DeepSeek API and print the resulting ob_ai_call and bl_ledger rows. services/vision and services/matting are placeholders noting that the local RTX 5090 makes BiRefNet and RTMPose developable without cloud GPU.
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# syntax=docker/dockerfile:1
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# API image (架构文档 §14.1 CVM-app / api). Multi-stage so the runtime layer
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# carries no build toolchain and no dev dependencies.
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FROM python:3.12-slim AS builder
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COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/
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ENV UV_COMPILE_BYTECODE=1 \
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UV_LINK_MODE=copy \
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UV_PYTHON_DOWNLOADS=never
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WORKDIR /app
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COPY pyproject.toml uv.lock* README.md ./
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RUN --mount=type=cache,target=/root/.cache/uv \
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uv sync --frozen --no-install-project --no-dev || uv sync --no-install-project --no-dev
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COPY apps ./apps
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COPY packages ./packages
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COPY services ./services
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COPY prompts ./prompts
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COPY migrations ./migrations
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COPY alembic.ini ./
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RUN --mount=type=cache,target=/root/.cache/uv \
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uv sync --no-dev --frozen || uv sync --no-dev
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FROM python:3.12-slim AS runtime
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ENV PYTHONUNBUFFERED=1 \
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PYTHONDONTWRITEBYTECODE=1 \
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PATH="/app/.venv/bin:$PATH" \
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TZ=Asia/Shanghai
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RUN groupadd --system meida && useradd --system --gid meida --home /app meida
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WORKDIR /app
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COPY --from=builder --chown=meida:meida /app /app
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USER meida
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EXPOSE 8000
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HEALTHCHECK --interval=15s --timeout=5s --start-period=20s --retries=3 \
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CMD python -c "import urllib.request;urllib.request.urlopen('http://localhost:8000/healthz')"
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# Gunicorn supervises uvicorn workers (架构文档 §5: FastAPI + Uvicorn/Gunicorn).
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CMD ["gunicorn", "apps.api.main:app", \
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"--worker-class", "uvicorn.workers.UvicornWorker", \
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"--workers", "4", \
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"--bind", "0.0.0.0:8000", \
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"--timeout", "120", \
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"--graceful-timeout", "30", \
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"--access-logfile", "-"]
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# syntax=docker/dockerfile:1
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# Celery worker image. Same build as the API; different entrypoint and queue set.
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# 架构文档 §14.1 runs worker-cpu / worker-io on CVM-app and worker-gpu on CVM-gpu,
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# so the queue list is injected rather than baked in.
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FROM python:3.12-slim AS builder
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COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/
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ENV UV_COMPILE_BYTECODE=1 \
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UV_LINK_MODE=copy \
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UV_PYTHON_DOWNLOADS=never
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WORKDIR /app
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COPY pyproject.toml uv.lock* README.md ./
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RUN --mount=type=cache,target=/root/.cache/uv \
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uv sync --frozen --no-install-project --no-dev || uv sync --no-install-project --no-dev
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COPY apps ./apps
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COPY packages ./packages
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COPY services ./services
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COPY prompts ./prompts
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COPY migrations ./migrations
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COPY alembic.ini ./
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RUN --mount=type=cache,target=/root/.cache/uv \
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uv sync --no-dev --frozen || uv sync --no-dev
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FROM python:3.12-slim AS runtime
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ENV PYTHONUNBUFFERED=1 \
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PYTHONDONTWRITEBYTECODE=1 \
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PATH="/app/.venv/bin:$PATH" \
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TZ=Asia/Shanghai \
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CELERY_QUEUES=cpu,io \
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CELERY_CONCURRENCY=4
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RUN groupadd --system meida && useradd --system --gid meida --home /app meida
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WORKDIR /app
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COPY --from=builder --chown=meida:meida /app /app
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USER meida
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HEALTHCHECK --interval=30s --timeout=10s --start-period=30s --retries=3 \
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CMD celery -A apps.worker.celery_app.celery_app inspect ping -d "celery@$(hostname)" || exit 1
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CMD ["sh", "-c", "celery -A apps.worker.celery_app.celery_app worker -Q ${CELERY_QUEUES} -c ${CELERY_CONCURRENCY} -l info"]
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# `deploy/`
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| 文件 | 用途 |
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| --- | --- |
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| `docker-compose.dev.yml` | 本地开发栈：PostgreSQL 16 + pgvector、Redis 7（均带 healthcheck） |
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| `Dockerfile.api` | API 镜像（gunicorn + uvicorn worker） |
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| `Dockerfile.worker` | Celery worker 镜像（队列由 `CELERY_QUEUES` 注入） |
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```bash
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make up # 只起 postgres + redis
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make down
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docker compose -f deploy/docker-compose.dev.yml --profile app up -d # 连 api/worker 一起
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```
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本地端口刻意用 **55432 / 56379**，避免和开发机上已有的 PG/Redis 撞车。
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## V1 生产形态（架构 §14.1）
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单可用区 + Docker Compose，不上 K8s（TD-03、团队规模）：
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```
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CLB → CVM-app: nginx / api / admin / worker-cpu / worker-io / beat / vision-svc
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CVM-gpu（按需启停）: tryon-svc / worker-gpu / matting
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云数据库 PostgreSQL(pgvector) · 云数据库 Redis · COS×4 + CDN · CLS
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```
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**GPU 实例按需启停是 V1 最有效的成本手段**：队列有积压时拉起，空闲一段时间后
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释放（NFR-COST-04）。
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## 规矩
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- **镜像里不许有密钥**（架构 §11.2）。配置一律从环境变量注入，生产走 SSM/KMS。
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`env_file: ../.env` 只在本地 compose 用。
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- 数据库迁移是发布流程里的**独立步骤**，先迁移后发布（§14.3）。
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- 镜像打标签化版本，不要用 `latest` 发布。
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- 容器以非 root 用户运行，两个 Dockerfile 都已如此。
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## 还没有的东西
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nginx 配置、TLS 终止、CLB/CDN 配置、COS 四桶的生命周期策略（临时桶 ≤ 2 小时，
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CR-02）、Grafana 三张看板、告警规则、备份与恢复演练脚本（FR-SYS-09）。
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Executable
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# Local development stack (T-P1-03: 新成员一条命令起环境).
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# Host ports are deliberately non-standard so this never collides with a
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# PostgreSQL/Redis already running on the developer's machine.
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name: meida-dev
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services:
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postgres:
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# pgvector is required from day one (架构文档 §5, TD-08) — proving the
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# extension exists here is what stops "works on my laptop" later.
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image: pgvector/pgvector:pg16
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container_name: meida-postgres
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environment:
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POSTGRES_USER: meida
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POSTGRES_PASSWORD: meida
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POSTGRES_DB: meida
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TZ: Asia/Shanghai
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ports:
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- "55432:5432"
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volumes:
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- pgdata:/var/lib/postgresql/data
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healthcheck:
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test: ["CMD-SHELL", "pg_isready -U meida -d meida"]
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interval: 5s
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timeout: 5s
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retries: 20
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start_period: 10s
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redis:
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image: redis:7-alpine
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container_name: meida-redis
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command: ["redis-server", "--appendonly", "yes"]
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ports:
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- "56379:6379"
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volumes:
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- redisdata:/data
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healthcheck:
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test: ["CMD", "redis-cli", "ping"]
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interval: 5s
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timeout: 3s
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retries: 20
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# --- optional: run the app itself in containers (`--profile app`) ---------
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api:
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profiles: ["app"]
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build:
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context: ..
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dockerfile: deploy/Dockerfile.api
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container_name: meida-api
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env_file: ../.env
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environment:
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DATABASE_URL: postgresql+asyncpg://meida:meida@postgres:5432/meida
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REDIS_URL: redis://redis:6379/0
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CELERY_BROKER_URL: redis://redis:6379/1
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CELERY_RESULT_BACKEND: redis://redis:6379/2
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ports:
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- "8000:8000"
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depends_on:
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postgres:
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condition: service_healthy
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redis:
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condition: service_healthy
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healthcheck:
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test: ["CMD", "python", "-c", "import urllib.request;urllib.request.urlopen('http://localhost:8000/healthz')"]
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interval: 10s
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timeout: 5s
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retries: 10
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worker-cpu:
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profiles: ["app"]
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build:
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context: ..
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dockerfile: deploy/Dockerfile.worker
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container_name: meida-worker-cpu
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env_file: ../.env
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environment:
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DATABASE_URL: postgresql+asyncpg://meida:meida@postgres:5432/meida
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REDIS_URL: redis://redis:6379/0
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CELERY_BROKER_URL: redis://redis:6379/1
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CELERY_RESULT_BACKEND: redis://redis:6379/2
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CELERY_QUEUES: cpu,io
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depends_on:
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postgres:
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condition: service_healthy
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redis:
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condition: service_healthy
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volumes:
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pgdata:
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redisdata:
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