Add accelerated_base Docker image with Intel QuickSync and NVIDIA CUDA support

- Ubuntu 22.04 base with Python 3.11
- Intel Media Driver and VAAPI for QuickSync hardware acceleration
- NVIDIA CUDA support via container toolkit
- FFmpeg with hardware codec support
- OpenCV, PyTorch, and common ML/CV packages
- Non-root user setup for security
- Provides comprehensive base for ML/CV applications with hardware acceleration

Dockerfile.accelerated_base

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+# Accelerated Base Image with Intel QuickSync, NVIDIA CUDA, FFmpeg, and Python support
+# Provides a comprehensive base for ML/CV applications with hardware acceleration
+FROM ubuntu:22.04
+
+LABEL maintainer="j"
+LABEL description="Base image with Intel QuickSync, NVIDIA CUDA support, FFmpeg, and Python 3.11"
+
+# Prevent interactive prompts during package installation
+ENV DEBIAN_FRONTEND=noninteractive
+
+# Install system dependencies including Intel and NVIDIA support
+RUN apt-get update && apt-get install -y \
+    # Base utilities
+    curl \
+    wget \
+    git \
+    build-essential \
+    pkg-config \
+    software-properties-common \
+    # Python 3.11
+    python3.11 \
+    python3.11-dev \
+    python3-pip \
+    python3.11-venv \
+    # FFmpeg with hardware acceleration support
+    ffmpeg \
+    # Intel QuickSync / VAAPI dependencies
+    intel-media-va-driver-non-free \
+    vainfo \
+    libva-drm2 \
+    libva-dev \
+    libva2 \
+    i965-va-driver \
+    # OpenGL/Graphics libraries
+    libgl1 \
+    libglib2.0-0 \
+    libsm6 \
+    libxext6 \
+    libxrender-dev \
+    libgomp1 \
+    libglu1-mesa \
+    libglu1-mesa-dev \
+    libgl1-mesa-dev \
+    libgl1-mesa-glx \
+    # OpenCV dependencies
+    libopencv-dev \
+    libgstreamer1.0-0 \
+    libgstreamer-plugins-base1.0-0 \
+    libgstreamer-plugins-bad1.0-0 \
+    gstreamer1.0-plugins-base \
+    gstreamer1.0-plugins-good \
+    gstreamer1.0-plugins-bad \
+    gstreamer1.0-plugins-ugly \
+    gstreamer1.0-libav \
+    gstreamer1.0-tools \
+    # Video codec libraries
+    libx264-dev \
+    libx265-dev \
+    libvpx-dev \
+    libfdk-aac-dev \
+    libmp3lame-dev \
+    libopus-dev \
+    # Image format libraries
+    libjpeg-dev \
+    libpng-dev \
+    libtiff-dev \
+    libwebp-dev \
+    # Additional ML/numeric libraries
+    libhdf5-dev \
+    libatlas-base-dev \
+    gfortran \
+    && rm -rf /var/lib/apt/lists/*
+
+# Set Python 3.11 as default
+RUN update-alternatives --install /usr/bin/python python /usr/bin/python3.11 1 && \
+    update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1 && \
+    update-alternatives --install /usr/bin/pip pip /usr/bin/pip3 1
+
+# Upgrade pip and install essential Python packages
+RUN python -m pip install --upgrade pip setuptools wheel
+
+# Install core Python ML/CV packages (CPU versions by default)
+# CUDA versions can be overlaid at runtime or in derived images
+RUN pip install --no-cache-dir \
+    numpy \
+    scipy \
+    pandas \
+    matplotlib \
+    pillow \
+    opencv-python-headless \
+    scikit-learn \
+    tqdm \
+    pyyaml \
+    requests
+
+# Install PyTorch CPU version (can be overridden in derived images for CUDA)
+RUN pip install --no-cache-dir \
+    torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
+
+# Set environment variables for hardware acceleration
+# Intel QuickSync / VAAPI
+ENV LIBVA_DRIVER_NAME=iHD
+ENV LIBVA_DRIVERS_PATH=/usr/lib/x86_64-linux-gnu/dri
+
+# Python environment
+ENV PYTHONUNBUFFERED=1
+ENV PYTHONDONTWRITEBYTECODE=1
+
+# OpenCV
+ENV OPENCV_VIDEOIO_PRIORITY_GSTREAMER=1
+
+# FFmpeg hardware acceleration preference
+ENV FFMPEG_HWACCEL_PRIORITY="vaapi,cuda,auto"
+
+# NVIDIA Container Toolkit environment variables (will be used if NVIDIA runtime is available)
+ENV NVIDIA_VISIBLE_DEVICES=all
+ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility,video
+
+# Create a non-root user for running applications
+RUN useradd -m -s /bin/bash -u 1000 appuser && \
+    usermod -a -G video appuser
+
+# Create common directories
+RUN mkdir -p /app /data /models && \
+    chown -R appuser:appuser /app /data /models
+
+# Set working directory
+WORKDIR /app
+
+# Health check command to verify Python and FFmpeg are working
+HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
+    CMD python -c "import torch, cv2, numpy; print('OK')" && ffmpeg -version > /dev/null 2>&1
+
+# Default to non-root user (can be overridden in derived images)
+USER appuser
+
+# Default command - can be overridden in derived images
+CMD ["/bin/bash"]

README.md

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-# debian-curl
+# Generic Docker Images
 
-Debian with Curl
+This repository contains reusable Docker base images for various purposes.
+
+## Available Images
+
+### debian-curl
+
+Debian with curl installed.
+
+### accelerated_base
+
+A comprehensive base image with hardware acceleration support for ML/CV applications.
+
+**Features:**
+- Ubuntu 22.04 LTS base
+- Python 3.11 with pip
+- Intel QuickSync / VAAPI support for hardware video acceleration
+- NVIDIA CUDA support (when NVIDIA Container Toolkit is available)
+- FFmpeg with hardware acceleration codecs
+- OpenCV with Python bindings
+- PyTorch (CPU by default, GPU when NVIDIA runtime is available)
+- Common ML/CV Python packages (numpy, scipy, pandas, matplotlib, etc.)
+- Video and image codec libraries
+- Non-root user setup for security
+
+**Usage:**
+```dockerfile
+FROM gitea.jde.nz/public/accelerated_base:latest
+
+# Your application-specific setup
+COPY your_app.py /app/
+CMD ["python", "/app/your_app.py"]
+```
+
+**Hardware Acceleration:**
+- Intel QuickSync: Automatically available when `/dev/dri` is mounted
+- NVIDIA GPU: Automatically available when using `--runtime=nvidia` or `--gpus all`
+
+**Environment Variables:**
+- `LIBVA_DRIVER_NAME=iHD` - Intel Media Driver for VAAPI
+- `FFMPEG_HWACCEL_PRIORITY="vaapi,cuda,auto"` - Hardware acceleration preference
+- `NVIDIA_VISIBLE_DEVICES=all` - NVIDIA GPU visibility
+- `NVIDIA_DRIVER_CAPABILITIES=compute,utility,video` - NVIDIA capabilities
+
+**Size:** ~2.5GB (includes Python, ML libraries, and video codecs)
+
+## Building
+
+Images are automatically built and published when pushed to this repository.
+
+## Registry
+
+Images are available at:
+- `gitea.jde.nz/public/<image_name>:latest`
+- `gitea.jde.nz/public/<image_name>:latest-<arch>` (architecture-specific)