# Ultralytics YOLO 🚀, AGPL-3.0 license
# Builds ultralytics/ultralytics:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv8 deployments

# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
FROM ubuntu:rolling

# 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' package
RUN apt update \
    && apt install --no-install-recommends -y python3-pip git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg g++
# RUN alias python=python3

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

# Copy contents
# COPY . /usr/src/app  (issues as not a .git directory)
RUN git clone https://github.com/ultralytics/ultralytics /usr/src/ultralytics
ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt /usr/src/ultralytics/

# Install pip packages
RUN python3 -m pip install --upgrade pip wheel
RUN pip install --no-cache . albumentations gsutil notebook tensorboard \
        --extra-index-url https://download.pytorch.org/whl/cpu


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

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

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