77a9d3b255
ffmpeg was missing from the base image, causing all pydub operations (AudioSegment.from_file, export) to fail in worker and tts-worker containers. Moved ffmpeg install from whisper-worker stage to the shared base stage so all container variants (api, worker, tts-worker, whisper-worker) have it. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
156 lines
6 KiB
Docker
156 lines
6 KiB
Docker
# =============================================================================
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# Multi-stage Dockerfile for Accessible Video Processing Platform
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# =============================================================================
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# Stage 1: Builder - Install dependencies
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# Stage 2: Base - Common runtime for API and Worker
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# Stage 3: API - FastAPI + Gunicorn (no ffmpeg — heavy tasks run on Cloud Run Jobs)
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# Stage 4: Worker - Celery worker, lightweight queues only (notify, embed)
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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==2.1.4
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# Configure Poetry to not create virtual environment (we're in a container)
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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 using Poetry directly (simpler and more reliable)
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RUN poetry config virtualenvs.create false \
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&& poetry install --only main --no-root --no-interaction --no-ansi \
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&& rm -rf $POETRY_CACHE_DIR
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# -----------------------------------------------------------------------------
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# Stage 2: Base - Common runtime environment
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# -----------------------------------------------------------------------------
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FROM python:3.11-slim AS base
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# Install common runtime dependencies
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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 (installed globally, not in user dir)
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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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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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# -----------------------------------------------------------------------------
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# Stage 3: API - FastAPI + Gunicorn (Production API Server)
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# Heavy pipeline tasks (ingest/translate/render) run on Cloud Run Jobs
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# -----------------------------------------------------------------------------
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FROM base AS api
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# Set production environment variables
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ENV APP_ENV=prod
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# Health check for API
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HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
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CMD curl -f http://localhost:8000/health || exit 1
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# Expose API port
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EXPOSE 8000
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# Use tini as init system for proper signal handling
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ENTRYPOINT ["tini", "--"]
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# Start Gunicorn with Uvicorn workers
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CMD ["gunicorn", "-c", "gunicorn_conf.py", "app.main:app"]
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# -----------------------------------------------------------------------------
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# Stage 4: Worker - Celery Worker (lightweight queues: notify, embed)
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# -----------------------------------------------------------------------------
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FROM base AS worker
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# Set production environment variables
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# WORKER_CONCURRENCY can be overridden at runtime (default: 8)
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ENV APP_ENV=prod \
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C_FORCE_ROOT=0 \
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WORKER_CONCURRENCY=8
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# Health check for worker (check if Celery is responding)
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HEALTHCHECK --interval=60s --timeout=15s --start-period=10s --retries=3 \
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CMD python -c "from celery import Celery; app=Celery('accessible-video-tasks', broker='redis://redis:6379/0'); app.control.inspect().ping() or exit(1)" || exit 1
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# Use tini as init system for proper signal handling
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ENTRYPOINT ["tini", "--"]
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# Start Celery worker listening to all queues EXCEPT whisper
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# Whisper has a dedicated worker (see Stage 5) to prevent memory overload
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# Concurrency is set via WORKER_CONCURRENCY env var (default: 8)
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# When using Cloud Run for FFmpeg, can increase to handle more parallel HTTP calls
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CMD celery -A celery_worker worker \
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-Q default,ingest,notify,render,ffmpeg \
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--loglevel=info \
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--concurrency=${WORKER_CONCURRENCY} \
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--max-tasks-per-child=100
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# -----------------------------------------------------------------------------
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# Stage 5: Whisper Worker - Dedicated worker for Whisper transcription
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# -----------------------------------------------------------------------------
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FROM base AS whisper-worker
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# Pre-download Whisper medium model during build to avoid cold start delays
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# Model is cached in ~/.cache/huggingface/hub (~1.5GB)
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RUN python -c "from faster_whisper import WhisperModel; WhisperModel('medium', device='cpu', compute_type='int8')"
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# Set production environment variables
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# WHISPER_WORKER_CONCURRENCY can be overridden at runtime
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# Default: 1 for local mode (RAM constrained), set to 10 for Cloud Run mode
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ENV APP_ENV=prod \
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C_FORCE_ROOT=0 \
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WHISPER_WORKER_CONCURRENCY=1
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# Health check for worker (check if Celery is responding)
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HEALTHCHECK --interval=60s --timeout=15s --start-period=10s --retries=3 \
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CMD python -c "from celery import Celery; app=Celery('accessible-video-tasks', broker='redis://redis:6379/0'); app.control.inspect().ping() or exit(1)" || exit 1
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# Use tini as init system for proper signal handling
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ENTRYPOINT ["tini", "--"]
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# Start Celery worker listening ONLY to whisper queue
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# Concurrency is set via WHISPER_WORKER_CONCURRENCY env var
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# Local mode: 1 (RAM constrained - Whisper model uses ~4-6GB)
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# Cloud Run mode: 10 (just HTTP calls, match Cloud Run max instances)
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# --max-tasks-per-child=50 to periodically recycle workers and free memory
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CMD celery -A celery_worker worker \
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-Q whisper \
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--loglevel=info \
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--concurrency=${WHISPER_WORKER_CONCURRENCY} \
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--max-tasks-per-child=50
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