# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

# Builds ultralytics/yolov5:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/yolov5
# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv5 deployments

# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
FROM ubuntu:24.04
ENV PIP_BREAK_SYSTEM_PACKAGES=1 \
    UV_BREAK_SYSTEM_PACKAGES=1

# Downloads to user config dir
ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/

# Install linux packages
# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package
RUN apt update \
    && apt install --no-install-recommends -y python3-pip git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0

# Install pip packages
COPY requirements.txt .
RUN python3 -m pip install uv wheel
RUN uv pip install --system --no-cache -r requirements.txt albumentations gsutil notebook \
    coremltools onnx onnxslim onnxruntime 'openvino>=2024.0.0' \
    --extra-index-url https://download.pytorch.org/whl/cpu \
    --index-strategy unsafe-best-match

# Create working directory
RUN mkdir -p /usr/src/app
WORKDIR /usr/src/app

# Copy contents
COPY . /usr/src/app


# Usage Examples -------------------------------------------------------------------------------------------------------

# Build and Push
# t=ultralytics/yolov5:latest-cpu && sudo docker build -f utils/docker/Dockerfile-cpu -t $t . && sudo docker push $t

# Pull and Run
# t=ultralytics/yolov5:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t
