bvandeusen
38942c211d
extract_video_frames() calls ffprobe/ffmpeg, which were only installed in the web Dockerfile. Videos route through the ml-worker for WD14 + SigLIP inference, so the tool needs to exist there too. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
43 lines
1.7 KiB
OCaml
43 lines
1.7 KiB
OCaml
# syntax=docker/dockerfile:1
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FROM python:3.12-slim AS base
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ENV PYTHONDONTWRITEBYTECODE=1 \
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PYTHONUNBUFFERED=1 \
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PIP_NO_CACHE_DIR=1 \
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ML_MODEL_DIR=/models
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# System deps: Pillow / image handling + psycopg2 runtime libpq + ffmpeg for
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# video frame sampling (WD14+SigLIP inference on videos calls ffprobe/ffmpeg).
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RUN apt-get update && apt-get install -y --no-install-recommends \
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libgl1 \
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libglib2.0-0 \
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libpq5 \
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ffmpeg \
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&& rm -rf /var/lib/apt/lists/*
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WORKDIR /app
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# Install PyTorch CPU wheel first from its dedicated index to avoid pulling CUDA libs.
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# Floor only — buildkit will pick the latest matching CPU wheel; keep this above the app-deps
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# layer so transformers on PyPI doesn't accidentally resolve a mismatched torch.
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RUN pip install --no-cache-dir \
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--index-url https://download.pytorch.org/whl/cpu \
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"torch>=2.5"
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# Install ML requirements. Models are NOT baked in — the entrypoint fetches them
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# at container start into the ${ML_MODEL_DIR} volume, and huggingface_hub's
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# content-based integrity check makes subsequent starts a no-op.
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COPY requirements-ml.txt /app/requirements-ml.txt
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RUN pip install --no-cache-dir -r requirements-ml.txt
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# Copy application code (needed so Celery can import app.tasks.ml and app.ml).
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# config.py lives at the repo root and is imported by app/__init__.py via 'config.Config'.
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COPY app /app/app
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COPY config.py /app/config.py
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COPY scripts/ensure_models.py /app/scripts/ensure_models.py
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COPY scripts/entrypoint-ml.sh /app/scripts/entrypoint-ml.sh
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RUN chmod +x /app/scripts/entrypoint-ml.sh && mkdir -p ${ML_MODEL_DIR}
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ENTRYPOINT ["/app/scripts/entrypoint-ml.sh"]
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CMD ["celery", "-A", "app.celery_app:celery", "worker", "--loglevel=info", "-Q", "ml", "--concurrency=1"]
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