mirror of
https://github.com/THU-MIG/yolov10.git
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41 lines
1.8 KiB
Plaintext
41 lines
1.8 KiB
Plaintext
# Ultralytics YOLO 🚀, AGPL-3.0 license
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# Builds ultralytics/ultralytics:latest-conda image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
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# Image is optimized for Ultralytics Anaconda (https://anaconda.org/conda-forge/ultralytics) installation and usage
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# Start FROM miniconda3 image https://hub.docker.com/r/continuumio/miniconda3
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FROM continuumio/miniconda3:latest
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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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# Install linux packages
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RUN apt update \
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&& apt install --no-install-recommends -y libgl1
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# Copy contents
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ADD https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8n.pt .
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# Install conda packages
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# mkl required to fix 'OSError: libmkl_intel_lp64.so.2: cannot open shared object file: No such file or directory'
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RUN conda config --set solver libmamba && \
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conda install pytorch torchvision pytorch-cuda=11.8 -c pytorch -c nvidia && \
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conda install -c conda-forge ultralytics mkl
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# conda install -c pytorch -c nvidia -c conda-forge pytorch torchvision pytorch-cuda=11.8 ultralytics mkl
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# Usage Examples -------------------------------------------------------------------------------------------------------
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# Build and Push
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# t=ultralytics/ultralytics:latest-conda && sudo docker build -f docker/Dockerfile-cpu -t $t . && sudo docker push $t
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# Run
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# t=ultralytics/ultralytics:latest-conda && sudo docker run -it --ipc=host $t
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# Pull and Run
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# t=ultralytics/ultralytics:latest-conda && sudo docker pull $t && sudo docker run -it --ipc=host $t
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# Pull and Run with local volume mounted
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# t=ultralytics/ultralytics:latest-conda && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t
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