Algorithm-Repo/SourceCode/MachineLearningUtils/VINNOFramework/YOLO/docker/Dockerfile-jetson-jetpack4

# Ultralytics YOLO 🚀, AGPL-3.0 license
# Builds ultralytics/ultralytics:jetson-jetpack4 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
# Supports JetPack4.x for YOLOv8 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 APP_HOME /usr/src/ultralytics

# 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
RUN apt update && \
    apt install --no-install-recommends -y git python3.8 python3.8-dev python3-pip python3-libnvinfer libopenmpi-dev libopenblas-base libomp-dev gcc

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

# Create working directory
WORKDIR $APP_HOME

# Copy contents and assign permissions
COPY . $APP_HOME
RUN chown -R root:root $APP_HOME
ADD https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt $APP_HOME

# Download onnxruntime-gpu, TensorRT, PyTorch and Torchvision
# Other versions can be seen in https://elinux.org/Jetson_Zoo and https://forums.developer.nvidia.com/t/pytorch-for-jetson/72048
ADD https://nvidia.box.com/shared/static/gjqofg7rkg97z3gc8jeyup6t8n9j8xjw.whl onnxruntime_gpu-1.8.0-cp38-cp38-linux_aarch64.whl
ADD https://forums.developer.nvidia.com/uploads/short-url/hASzFOm9YsJx6VVFrDW1g44CMmv.whl tensorrt-8.2.0.6-cp38-none-linux_aarch64.whl
ADD https://github.com/ultralytics/yolov5/releases/download/v1.0/torch-1.11.0a0+gitbc2c6ed-cp38-cp38-linux_aarch64.whl \
    torch-1.11.0a0+gitbc2c6ed-cp38-cp38-linux_aarch64.whl
ADD https://github.com/ultralytics/yolov5/releases/download/v1.0/torchvision-0.12.0a0+9b5a3fe-cp38-cp38-linux_aarch64.whl \
    torchvision-0.12.0a0+9b5a3fe-cp38-cp38-linux_aarch64.whl

# Install pip packages
RUN python3 -m pip install --upgrade pip wheel
RUN pip install onnxruntime_gpu-1.8.0-cp38-cp38-linux_aarch64.whl tensorrt-8.2.0.6-cp38-none-linux_aarch64.whl \
    torch-1.11.0a0+gitbc2c6ed-cp38-cp38-linux_aarch64.whl torchvision-0.12.0a0+9b5a3fe-cp38-cp38-linux_aarch64.whl
RUN pip install --no-cache-dir -e ".[export]"

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

# Build and Push
# t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker build --platform linux/arm64 -f docker/Dockerfile-jetson-jetpack4 -t $t . && sudo docker push $t

# Run
# t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker run -it --ipc=host $t

# Pull and Run
# t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker pull $t && sudo docker run -it --ipc=host $t

# Pull and Run with NVIDIA runtime
# t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t