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# Ultralytics YOLO 🚀, AGPL-3.0 license
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# Builds ultralytics/ultralytics:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
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# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv8 deployments
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# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
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FROM ubuntu:rolling
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# Downloads to user config dir
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ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
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# Install linux packages
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# g++ required to build 'tflite_support' package
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RUN apt update \
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&& apt install --no-install-recommends -y python3-pip git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg g++
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# RUN alias python=python3
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# Create working directory
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RUN mkdir -p /usr/src/ultralytics
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WORKDIR /usr/src/ultralytics
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# Copy contents
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# COPY . /usr/src/app (issues as not a .git directory)
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RUN git clone https://github.com/ultralytics/ultralytics /usr/src/ultralytics
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ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt /usr/src/ultralytics/
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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 --no-cache . albumentations gsutil notebook tensorboard \
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--extra-index-url https://download.pytorch.org/whl/cpu
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# Usage Examples -------------------------------------------------------------------------------------------------------
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# Build and Push
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# t=ultralytics/ultralytics:latest-cpu && sudo docker build -f docker/Dockerfile-cpu -t $t . && sudo docker push $t
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# Pull and Run
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# t=ultralytics/ultralytics:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t
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