# GPU-enabled worker image
# Based on NVIDIA CUDA with Python for CuPy support

FROM nvidia/cuda:12.1.1-cudnn8-runtime-ubuntu22.04

WORKDIR /app

# Install Python 3.11 and system dependencies
RUN apt-get update && apt-get install -y --no-install-recommends \
    python3.11 \
    python3.11-venv \
    python3-pip \
    git \
    ffmpeg \
    && rm -rf /var/lib/apt/lists/* \
    && ln -sf /usr/bin/python3.11 /usr/bin/python3 \
    && ln -sf /usr/bin/python3 /usr/bin/python

# Upgrade pip
RUN python3 -m pip install --upgrade pip

# Install CPU dependencies first
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Install GPU-specific dependencies (CuPy for CUDA 12.x)
RUN pip install --no-cache-dir cupy-cuda12x

# Install decord for GPU-accelerated video decoding (keeps frames on GPU)
# This avoids CPU<->GPU memory transfers during video decode
RUN pip install --no-cache-dir decord

# Copy application
COPY . .

# Clone effects repo
RUN git clone https://git.rose-ash.com/art-dag/effects.git /app/artdag-effects

# Create cache directory
RUN mkdir -p /data/cache

ENV PYTHONUNBUFFERED=1
ENV PYTHONDONTWRITEBYTECODE=1
ENV EFFECTS_PATH=/app/artdag-effects
ENV PYTHONPATH=/app
# GPU persistence enabled - frames stay on GPU throughout pipeline
ENV STREAMING_GPU_PERSIST=1
# Use cluster's public IPFS gateway for HLS segment URLs
ENV IPFS_GATEWAY_URL=https://celery-artdag.rose-ash.com/ipfs

# Default command runs celery worker
CMD ["celery", "-A", "celery_app", "worker", "--loglevel=info", "-E", "-Q", "gpu,celery"]