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| # 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 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 | |
| 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 /ultralytics | |
| # Copy contents and configure git | |
| COPY . . | |
| RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config | |
| ADD https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt . | |
| # Download onnxruntime-gpu 1.8.0 and tensorrt 8.2.0.6 | |
| # 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 | |
| # 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 \ | |
| 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 | |
| RUN pip install -e ".[export]" | |
| RUN rm *.whl | |
| # 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 | |