79440929f4
Migrate CPU-intensive workloads to Cloud Run for autoscaling: - Add Whisper HTTP service (FastAPI) with /transcribe endpoint - Add FFmpeg HTTP service (FastAPI) with /encode, /probe, /extract-frame, etc. - Add Dockerfiles for both services (8 vCPU, 32GB RAM, Gen2) - Add Cloud Build config for CI/CD deployment - Add Cloud Run service YAML configs with scale-to-zero - Update whisper_transcribe.py to call Cloud Run when WHISPER_SERVICE_URL set - Update video_renderer.py to call Cloud Run when FFMPEG_SERVICE_URL set - Update whisper_service.py for Cloud Run compatibility (no settings dependency) - Add ffmpeg_service_url and whisper_service_url to config.py Services scale 0→N based on request load, falling back to local execution when service URLs are not configured (hybrid mode). 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
97 lines
3.1 KiB
Text
97 lines
3.1 KiB
Text
# =============================================================================
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# Dockerfile for FFmpeg HTTP Service - Cloud Run Deployment
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# =============================================================================
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# This Dockerfile creates a FastAPI-based HTTP service for FFmpeg operations,
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# designed for deployment on Google Cloud Run with autoscaling.
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#
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# Key features:
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# - Lightweight image with FFmpeg + FastAPI (no Whisper model)
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# - Fast startup for quick autoscaling
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# - Optimized for 8 vCPU / 32GB RAM Cloud Run instances
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# =============================================================================
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# -----------------------------------------------------------------------------
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# Stage 1: Builder - Install Python dependencies using Poetry
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# -----------------------------------------------------------------------------
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FROM python:3.11-slim AS builder
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# Install build dependencies
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RUN apt-get update && apt-get install -y --no-install-recommends \
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build-essential \
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curl \
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&& rm -rf /var/lib/apt/lists/*
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# Install Poetry
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RUN pip install --no-cache-dir poetry==1.8.2
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# Configure Poetry
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ENV POETRY_NO_INTERACTION=1 \
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POETRY_VIRTUALENVS_CREATE=false \
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POETRY_CACHE_DIR=/tmp/poetry_cache
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WORKDIR /app
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# Copy dependency files
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COPY pyproject.toml poetry.lock ./
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# Install dependencies (exclude faster-whisper to keep image small)
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# Note: We still install all deps for simplicity, but could optimize with groups
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RUN poetry config virtualenvs.create false \
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&& poetry install --only main --no-interaction --no-ansi \
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&& rm -rf $POETRY_CACHE_DIR
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# -----------------------------------------------------------------------------
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# Stage 2: Runtime - FFmpeg HTTP Service
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# -----------------------------------------------------------------------------
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FROM python:3.11-slim AS runtime
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# Install runtime dependencies including FFmpeg
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RUN apt-get update && apt-get install -y --no-install-recommends \
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libmagic1 \
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curl \
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tini \
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ffmpeg \
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&& rm -rf /var/lib/apt/lists/* \
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&& apt-get clean
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# Create non-root user for security
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RUN groupadd --gid 1000 app \
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&& useradd --uid 1000 --gid app --shell /bin/bash --create-home app
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# Copy Python packages from builder
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COPY --from=builder /usr/local/lib/python3.11/site-packages /usr/local/lib/python3.11/site-packages
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COPY --from=builder /usr/local/bin /usr/local/bin
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# Set environment variables
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ENV PYTHONPATH=/app \
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PYTHONUNBUFFERED=1 \
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PYTHONDONTWRITEBYTECODE=1 \
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APP_ENV=prod
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WORKDIR /app
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# Copy application code
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COPY --chown=app:app . .
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# Switch to non-root user
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USER app
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# Health check
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HEALTHCHECK --interval=30s --timeout=10s --start-period=10s --retries=3 \
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CMD curl -f http://localhost:8080/health || exit 1
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# Expose HTTP port (Cloud Run uses 8080 by default)
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EXPOSE 8080
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# Use tini as init system
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ENTRYPOINT ["tini", "--"]
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# Start Uvicorn server
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# - 1 worker since Cloud Run uses containerConcurrency=1
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# - Bind to 0.0.0.0:8080 for Cloud Run
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# - Timeout of 300s for long FFmpeg operations
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CMD ["uvicorn", "app.services.ffmpeg_http_service:app", \
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"--host", "0.0.0.0", \
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"--port", "8080", \
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"--workers", "1", \
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"--timeout-keep-alive", "300"]
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