Add accelerated_base Docker image with Intel QuickSync and NVIDIA CUDA support
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- 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
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Dockerfile.accelerated_base
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Dockerfile.accelerated_base
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# Accelerated Base Image with Intel QuickSync, NVIDIA CUDA, FFmpeg, and Python support
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# Provides a comprehensive base for ML/CV applications with hardware acceleration
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FROM ubuntu:22.04
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LABEL maintainer="j"
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LABEL description="Base image with Intel QuickSync, NVIDIA CUDA support, FFmpeg, and Python 3.11"
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# Prevent interactive prompts during package installation
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ENV DEBIAN_FRONTEND=noninteractive
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# Install system dependencies including Intel and NVIDIA support
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RUN apt-get update && apt-get install -y \
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# Base utilities
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curl \
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wget \
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git \
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build-essential \
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pkg-config \
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software-properties-common \
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# Python 3.11
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python3.11 \
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python3.11-dev \
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python3-pip \
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python3.11-venv \
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# FFmpeg with hardware acceleration support
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ffmpeg \
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# Intel QuickSync / VAAPI dependencies
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intel-media-va-driver-non-free \
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vainfo \
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libva-drm2 \
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libva-dev \
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libva2 \
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i965-va-driver \
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# OpenGL/Graphics libraries
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libgl1 \
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libglib2.0-0 \
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libsm6 \
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libxext6 \
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libxrender-dev \
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libgomp1 \
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libglu1-mesa \
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libglu1-mesa-dev \
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libgl1-mesa-dev \
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libgl1-mesa-glx \
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# OpenCV dependencies
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libopencv-dev \
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libgstreamer1.0-0 \
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libgstreamer-plugins-base1.0-0 \
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libgstreamer-plugins-bad1.0-0 \
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gstreamer1.0-plugins-base \
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gstreamer1.0-plugins-good \
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gstreamer1.0-plugins-bad \
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gstreamer1.0-plugins-ugly \
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gstreamer1.0-libav \
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gstreamer1.0-tools \
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# Video codec libraries
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libx264-dev \
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libx265-dev \
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libvpx-dev \
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libfdk-aac-dev \
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libmp3lame-dev \
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libopus-dev \
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# Image format libraries
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libjpeg-dev \
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libpng-dev \
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libtiff-dev \
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libwebp-dev \
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# Additional ML/numeric libraries
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libhdf5-dev \
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libatlas-base-dev \
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gfortran \
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&& rm -rf /var/lib/apt/lists/*
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# Set Python 3.11 as default
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RUN update-alternatives --install /usr/bin/python python /usr/bin/python3.11 1 && \
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update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1 && \
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update-alternatives --install /usr/bin/pip pip /usr/bin/pip3 1
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# Upgrade pip and install essential Python packages
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RUN python -m pip install --upgrade pip setuptools wheel
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# Install core Python ML/CV packages (CPU versions by default)
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# CUDA versions can be overlaid at runtime or in derived images
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RUN pip install --no-cache-dir \
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numpy \
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scipy \
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pandas \
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matplotlib \
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pillow \
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opencv-python-headless \
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scikit-learn \
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tqdm \
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pyyaml \
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requests
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# Install PyTorch CPU version (can be overridden in derived images for CUDA)
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RUN pip install --no-cache-dir \
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torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
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# Set environment variables for hardware acceleration
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# Intel QuickSync / VAAPI
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ENV LIBVA_DRIVER_NAME=iHD
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ENV LIBVA_DRIVERS_PATH=/usr/lib/x86_64-linux-gnu/dri
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# Python environment
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ENV PYTHONUNBUFFERED=1
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ENV PYTHONDONTWRITEBYTECODE=1
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# OpenCV
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ENV OPENCV_VIDEOIO_PRIORITY_GSTREAMER=1
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# FFmpeg hardware acceleration preference
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ENV FFMPEG_HWACCEL_PRIORITY="vaapi,cuda,auto"
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# NVIDIA Container Toolkit environment variables (will be used if NVIDIA runtime is available)
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ENV NVIDIA_VISIBLE_DEVICES=all
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ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility,video
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# Create a non-root user for running applications
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RUN useradd -m -s /bin/bash -u 1000 appuser && \
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usermod -a -G video appuser
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# Create common directories
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RUN mkdir -p /app /data /models && \
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chown -R appuser:appuser /app /data /models
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# Set working directory
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WORKDIR /app
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# Health check command to verify Python and FFmpeg are working
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HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
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CMD python -c "import torch, cv2, numpy; print('OK')" && ffmpeg -version > /dev/null 2>&1
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# Default to non-root user (can be overridden in derived images)
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USER appuser
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# Default command - can be overridden in derived images
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CMD ["/bin/bash"]
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57
README.md
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README.md
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# debian-curl
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# Generic Docker Images
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Debian with Curl
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This repository contains reusable Docker base images for various purposes.
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## Available Images
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### debian-curl
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Debian with curl installed.
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### accelerated_base
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A comprehensive base image with hardware acceleration support for ML/CV applications.
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**Features:**
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- Ubuntu 22.04 LTS base
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- Python 3.11 with pip
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- Intel QuickSync / VAAPI support for hardware video acceleration
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- NVIDIA CUDA support (when NVIDIA Container Toolkit is available)
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- FFmpeg with hardware acceleration codecs
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- OpenCV with Python bindings
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- PyTorch (CPU by default, GPU when NVIDIA runtime is available)
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- Common ML/CV Python packages (numpy, scipy, pandas, matplotlib, etc.)
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- Video and image codec libraries
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- Non-root user setup for security
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**Usage:**
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```dockerfile
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FROM gitea.jde.nz/public/accelerated_base:latest
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# Your application-specific setup
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COPY your_app.py /app/
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CMD ["python", "/app/your_app.py"]
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```
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**Hardware Acceleration:**
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- Intel QuickSync: Automatically available when `/dev/dri` is mounted
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- NVIDIA GPU: Automatically available when using `--runtime=nvidia` or `--gpus all`
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**Environment Variables:**
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- `LIBVA_DRIVER_NAME=iHD` - Intel Media Driver for VAAPI
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- `FFMPEG_HWACCEL_PRIORITY="vaapi,cuda,auto"` - Hardware acceleration preference
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- `NVIDIA_VISIBLE_DEVICES=all` - NVIDIA GPU visibility
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- `NVIDIA_DRIVER_CAPABILITIES=compute,utility,video` - NVIDIA capabilities
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**Size:** ~2.5GB (includes Python, ML libraries, and video codecs)
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## Building
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Images are automatically built and published when pushed to this repository.
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## Registry
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Images are available at:
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- `gitea.jde.nz/public/<image_name>:latest`
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- `gitea.jde.nz/public/<image_name>:latest-<arch>` (architecture-specific)
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