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			1.8 KiB
		
	
	
	
		
			Plaintext
		
	
	
	
	
	
			
		
		
	
	
			42 lines
		
	
	
		
			1.8 KiB
		
	
	
	
		
			Plaintext
		
	
	
	
	
	
| # Ultralytics YOLO 🚀, AGPL-3.0 license
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| # Builds ultralytics/ultralytics:jetson image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
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| # Supports JetPack for YOLOv8 on Jetson Nano, TX1/TX2, Xavier NX, AGX Xavier, AGX Orin, and Orin NX
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| 
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| # Start FROM https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-pytorch
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| FROM nvcr.io/nvidia/l4t-pytorch:r35.2.1-pth2.0-py3
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| 
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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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| 
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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 gcc 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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| 
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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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| 
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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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| 
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| # Install pip packages manually for TensorRT compatibility https://github.com/NVIDIA/TensorRT/issues/2567
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| RUN python3 -m pip install --upgrade pip wheel
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| RUN pip install --no-cache tqdm matplotlib pyyaml psutil thop pandas onnx "numpy==1.23"
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| RUN pip install --no-cache -e .
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| 
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| # Set environment variables
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| ENV OMP_NUM_THREADS=1
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| 
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| 
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| # Usage Examples -------------------------------------------------------------------------------------------------------
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| 
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| # Build and Push
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| # t=ultralytics/ultralytics:latest-jetson && sudo docker build --platform linux/arm64 -f docker/Dockerfile-jetson -t $t . && sudo docker push $t
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| 
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| # Pull and Run
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| # t=ultralytics/ultralytics:jetson && sudo docker pull $t && sudo docker run -it --runtime=nvidia $t
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