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8a4720472b
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.
51 lines
1.5 KiB
Docker
Executable File
51 lines
1.5 KiB
Docker
Executable File
# 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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