Add IPFS HLS streaming and GPU optimizations

- Add IPFSHLSOutput class that uploads segments to IPFS as they're created
- Update streaming task to use IPFS HLS output for distributed streaming
- Add /ipfs-stream endpoint to get IPFS playlist URL
- Update /stream endpoint to redirect to IPFS when available
- Add GPU persistence mode (STREAMING_GPU_PERSIST=1) to keep frames on GPU
- Add hardware video decoding (NVDEC) support for faster video processing
- Add GPU-accelerated primitive libraries: blending_gpu, color_ops_gpu, geometry_gpu
- Add streaming_gpu module with GPUFrame class for tracking CPU/GPU data location
- Add Dockerfile.gpu for building GPU-enabled worker image

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Dockerfile.gpu

@@ -0,0 +1,44 @@
+# 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
+
+# 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
+
+# Default command runs celery worker
+CMD ["celery", "-A", "celery_app", "worker", "--loglevel=info", "-E", "-Q", "gpu,celery"]