mirror of
https://github.com/ultralytics/ultralytics
synced 2026-04-21 14:07:18 +00:00
73 lines
3.8 KiB
Text
73 lines
3.8 KiB
Text
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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# Builds ultralytics/ultralytics:latest-jetson-jetpack4 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
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# Supports JetPack4.x for YOLO 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 PYTHONUNBUFFERED=1 \
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PYTHONDONTWRITEBYTECODE=1
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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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# pkg-config and libhdf5-dev (not included) are needed to build 'h5py==3.11.0' aarch64 wheel required by 'tensorflow'
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# gnupg required for Edge TPU install
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RUN apt-get update && \
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apt-get install -y --no-install-recommends \
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git python3.8 python3.8-dev python3-pip python3-libnvinfer libopenmpi-dev libopenblas-base libomp-dev gcc \
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&& rm -rf /var/lib/apt/lists/*
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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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ln -s /usr/bin/pip3 /usr/bin/pip
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# Create working directory
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WORKDIR /ultralytics
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# Copy contents and configure git
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COPY . .
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RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config && \
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sed -i'' -e 's/"opencv-python/"opencv-python-headless/' pyproject.toml
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ADD https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt .
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# Replace pyproject.toml TF.js version with 'tensorflowjs>=3.9.0' for JetPack4 compatibility
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RUN sed -i 's/^\( *"tensorflowjs\)>=.*\(".*\)/\1>=3.9.0\2/' pyproject.toml
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# Install pip packages (pip must be upgraded first before installing uv due to missing setuptools)
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RUN python3 -m pip install --upgrade pip && \
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python3 -m pip install uv
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# Install pip packages and remove extra build files
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# Onnxruntime and TensorRT from https://elinux.org/Jetson_Zoo and https://forums.developer.nvidia.com/t/pytorch-for-jetson/72048
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RUN uv pip install --system \
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https://github.com/ultralytics/assets/releases/download/v0.0.0/onnxruntime_gpu-1.8.0-cp38-cp38-linux_aarch64.whl \
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https://github.com/ultralytics/assets/releases/download/v0.0.0/tensorrt-8.2.0.6-cp38-none-linux_aarch64.whl \
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https://github.com/ultralytics/assets/releases/download/v0.0.0/torch-1.11.0a0+gitbc2c6ed-cp38-cp38-linux_aarch64.whl \
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https://github.com/ultralytics/assets/releases/download/v0.0.0/torchvision-0.12.0a0+9b5a3fe-cp38-cp38-linux_aarch64.whl && \
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uv pip install --system -e ".[export]" && \
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# Remove extra build files
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rm -rf *.whl /root/.config/Ultralytics/persistent_cache.json
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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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