Dockerfile

# Use NVIDIA CUDA base image with Python support
FROM nvidia/cuda:12.4.0-runtime-ubuntu22.04

# Set up environment
ENV DEBIAN_FRONTEND=noninteractive
ENV PYTHONUNBUFFERED=1

WORKDIR /app

# Install Python and system dependencies
RUN apt-get update && apt-get install -y --no-install-recommends \
    python3.10 \
    python3.10-venv \
    python3.10-dev \
    python3-pip \
    build-essential \
    ffmpeg \
    libsm6 \
    libxext6 \
    git \
    curl \
    ca-certificates \
    && rm -rf /var/lib/apt/lists/*

# Install Node.js 20 (yt-dlp requires >= 20 for JS challenge solving)
RUN curl -fsSL https://deb.nodesource.com/setup_20.x | bash - \
    && apt-get install -y --no-install-recommends nodejs \
    && rm -rf /var/lib/apt/lists/*

# Create virtual environment and install Python dependencies
RUN python3 -m venv /venv
# Add venv to PATH
ENV VENV_PATH=/venv
ENV PATH="/venv/bin:$PATH"

# Upgrade pip and install PyTorch with CUDA support
RUN /venv/bin/pip install --no-cache-dir --upgrade pip
RUN /venv/bin/pip install --no-cache-dir torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124

# Copy requirements file
COPY requirements.txt /app/
RUN /venv/bin/pip install --no-cache-dir -r requirements.txt

# Copy and install module-specific requirements (each module may have its own requirements.txt)
# Change MODULE_REQS_CACHE_BUST value to force reinstall: --build-arg MODULE_REQS_CACHE_BUST=$(date +%s)
ARG MODULE_REQS_CACHE_BUST=1
COPY modules/*/requirements.txt /tmp/module_reqs/
RUN find /tmp/module_reqs/ -name 'requirements.txt' -exec /venv/bin/pip install --no-cache-dir -r {} \; || true
RUN rm -rf /tmp/module_reqs

# Expose the application port
EXPOSE 5001

# Create logs directory
#RUN mkdir -p /app/logs

# Running flask app from app.py
#CMD /venv/bin/python app.py > /app/logs/${CONTAINER_NAME:-container}_log.txt 2>&1
CMD ["bash", "-c", "/venv/bin/python app.py > /app/logs/${CONTAINER_NAME:-container}_log.txt 2>&1"]