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

# Builds ultralytics/ultralytics:latest-jetson-jetpack4 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
# Supports JetPack4.x for YOLO on Jetson Nano, TX2, Xavier NX, AGX Xavier

# Start FROM https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-cuda
FROM nvcr.io/nvidia/l4t-cuda:10.2.460-runtime

# Set environment variables
ENV PYTHONUNBUFFERED=1 \
    PYTHONDONTWRITEBYTECODE=1

# Downloads to user config dir
ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \
    https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \
    /root/.config/Ultralytics/

# Add NVIDIA repositories for TensorRT dependencies
RUN wget -q -O - https://repo.download.nvidia.com/jetson/jetson-ota-public.asc | apt-key add - && \
  echo "deb https://repo.download.nvidia.com/jetson/common r32.7 main" > /etc/apt/sources.list.d/nvidia-l4t-apt-source.list && \
  echo "deb https://repo.download.nvidia.com/jetson/t194 r32.7 main" >> /etc/apt/sources.list.d/nvidia-l4t-apt-source.list

# Install dependencies
# pkg-config and libhdf5-dev (not included) are needed to build 'h5py==3.11.0' aarch64 wheel required by 'tensorflow'
# gnupg required for Edge TPU install
RUN apt-get update && \
    apt-get install -y --no-install-recommends \
    git python3.8 python3.8-dev python3-pip python3-libnvinfer libopenmpi-dev libopenblas-base libomp-dev gcc && \
    apt-get clean && \
    rm -rf /var/lib/apt/lists/*

# Create symbolic links for python3.8 and pip3
RUN ln -sf /usr/bin/python3.8 /usr/bin/python3 && \
    ln -sf /usr/bin/pip3 /usr/bin/pip

# Create working directory
WORKDIR /ultralytics

# Copy contents and configure git
COPY . .
RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config && \
    sed -i'' -e 's/"opencv-python/"opencv-python-headless/' pyproject.toml
ADD https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n.pt .

# Replace pyproject.toml TF.js version with 'tensorflowjs>=3.9.0' for JetPack4 compatibility
RUN sed -i 's/^\( *"tensorflowjs\)>=.*\(".*\)/\1>=3.9.0\2/' pyproject.toml

# Install pip packages (pip must be upgraded first before installing uv due to missing setuptools)
RUN python3 -m pip install --upgrade pip && \
    python3 -m pip install uv
# Install pip packages and remove extra build files
# Onnxruntime and TensorRT from https://elinux.org/Jetson_Zoo and https://forums.developer.nvidia.com/t/pytorch-for-jetson/72048
RUN uv pip install --system \
    https://github.com/ultralytics/assets/releases/download/v0.0.0/onnxruntime_gpu-1.8.0-cp38-cp38-linux_aarch64.whl \
    https://github.com/ultralytics/assets/releases/download/v0.0.0/tensorrt-8.2.0.6-cp38-none-linux_aarch64.whl \
    https://github.com/ultralytics/assets/releases/download/v0.0.0/torch-1.11.0a0+gitbc2c6ed-cp38-cp38-linux_aarch64.whl \
    https://github.com/ultralytics/assets/releases/download/v0.0.0/torchvision-0.12.0a0+9b5a3fe-cp38-cp38-linux_aarch64.whl && \
    uv pip install --system -e ".[export]" && \
    # Remove extra build files
    rm -rf *.whl /root/.config/Ultralytics/persistent_cache.json

# Usage --------------------------------------------------------------------------------------------------------------

# Production builds: https://github.com/ultralytics/ultralytics/blob/main/.github/workflows/docker.yml
# Example (build): t=ultralytics/ultralytics:latest-jetson-jetpack4 && docker build --platform linux/arm64 -f docker/Dockerfile-jetson-jetpack4 -t $t .
# Example (push): docker push $t
# Example (pull): t=ultralytics/ultralytics:latest-jetson-jetpack4 && docker pull $t
# Example (run): docker run -it --ipc=host --runtime=nvidia $t
# Example (run-with-volume): docker run -it --ipc=host --runtime=nvidia -v "$PWD/shared/datasets:/datasets" $t