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# Ultralytics YOLO 🚀, AGPL-3.0 license
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# Builds ultralytics/ultralytics:jetson-jetpack4 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
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# Supports JetPack4.x for YOLOv8 on Jetson Nano, TX2, Xavier NX, AGX Xavier
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# Start FROM https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-cuda
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FROM nvcr.io/nvidia/l4t-cuda:10.2.460-runtime
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# Set environment variables
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ENV APP_HOME /usr/src/ultralytics
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# Downloads to user config dir
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ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \
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https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \
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/root/.config/Ultralytics/
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# Add NVIDIA repositories for TensorRT dependencies
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RUN wget -q -O - https://repo.download.nvidia.com/jetson/jetson-ota-public.asc | apt-key add - && \
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echo "deb https://repo.download.nvidia.com/jetson/common r32.7 main" > /etc/apt/sources.list.d/nvidia-l4t-apt-source.list && \
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echo "deb https://repo.download.nvidia.com/jetson/t194 r32.7 main" >> /etc/apt/sources.list.d/nvidia-l4t-apt-source.list
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# Install dependencies
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RUN apt update && \
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apt install --no-install-recommends -y git python3.8 python3.8-dev python3-pip python3-libnvinfer libopenmpi-dev libopenblas-base libomp-dev gcc
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# Create symbolic links for python3.8 and pip3
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RUN ln -sf /usr/bin/python3.8 /usr/bin/python3
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RUN ln -s /usr/bin/pip3 /usr/bin/pip
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# Create working directory
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WORKDIR $APP_HOME
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# Copy contents and assign permissions
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COPY . $APP_HOME
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RUN chown -R root:root $APP_HOME
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ADD https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt $APP_HOME
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# Download onnxruntime-gpu, TensorRT, PyTorch and Torchvision
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# Other versions can be seen in https://elinux.org/Jetson_Zoo and https://forums.developer.nvidia.com/t/pytorch-for-jetson/72048
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ADD https://nvidia.box.com/shared/static/gjqofg7rkg97z3gc8jeyup6t8n9j8xjw.whl onnxruntime_gpu-1.8.0-cp38-cp38-linux_aarch64.whl
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ADD https://forums.developer.nvidia.com/uploads/short-url/hASzFOm9YsJx6VVFrDW1g44CMmv.whl tensorrt-8.2.0.6-cp38-none-linux_aarch64.whl
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ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/torch-1.11.0a0+gitbc2c6ed-cp38-cp38-linux_aarch64.whl \
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torch-1.11.0a0+gitbc2c6ed-cp38-cp38-linux_aarch64.whl
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ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/torchvision-0.12.0a0+9b5a3fe-cp38-cp38-linux_aarch64.whl \
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torchvision-0.12.0a0+9b5a3fe-cp38-cp38-linux_aarch64.whl
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# Install pip packages
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RUN python3 -m pip install --upgrade pip wheel
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RUN pip install onnxruntime_gpu-1.8.0-cp38-cp38-linux_aarch64.whl tensorrt-8.2.0.6-cp38-none-linux_aarch64.whl \
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torch-1.11.0a0+gitbc2c6ed-cp38-cp38-linux_aarch64.whl torchvision-0.12.0a0+9b5a3fe-cp38-cp38-linux_aarch64.whl
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RUN pip install --no-cache-dir -e ".[export]"
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# Usage Examples -------------------------------------------------------------------------------------------------------
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# Build and Push
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# t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker build --platform linux/arm64 -f docker/Dockerfile-jetson-jetpack4 -t $t . && sudo docker push $t
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# Run
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# t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker run -it --ipc=host $t
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# Pull and Run
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# t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker pull $t && sudo docker run -it --ipc=host $t
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# Pull and Run with NVIDIA runtime
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# t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t
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