81 lines
3.3 KiB
Docker
81 lines
3.3 KiB
Docker
# Ultralytics YOLO 🚀, AGPL-3.0 license
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# Builds ultralytics/ultralytics:latest image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
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# Image is CUDA-optimized for YOLOv8 single/multi-GPU training and inference
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# Start FROM PyTorch image https://hub.docker.com/r/pytorch/pytorch or nvcr.io/nvidia/pytorch:23.03-py3
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FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime
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RUN pip install --no-cache nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com
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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' and 'lap' packages, libusb-1.0-0 required for '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++ libusb-1.0-0
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# RUN alias python=python3
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# Security updates
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# https://security.snyk.io/vuln/SNYK-UBUNTU1804-OPENSSL-3314796
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RUN apt upgrade --no-install-recommends -y openssl tar
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# Create working directory
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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 -e ".[export]" thop albumentations comet pycocotools
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# Run exports to AutoInstall packages
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RUN yolo export model=tmp/yolov8n.pt format=edgetpu imgsz=32
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RUN yolo export model=tmp/yolov8n.pt format=ncnn imgsz=32
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# Requires <= Python 3.10, bug with paddlepaddle==2.5.0
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RUN pip install --no-cache paddlepaddle==2.4.2 x2paddle
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# Fix error: `np.bool` was a deprecated alias for the builtin `bool`
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RUN pip install --no-cache numpy==1.23.5
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# Remove exported models
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RUN rm -rf tmp
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# Set environment variables
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ENV OMP_NUM_THREADS=1
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# Avoid DDP error "MKL_THREADING_LAYER=INTEL is incompatible with libgomp.so.1 library" https://github.com/pytorch/pytorch/issues/37377
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ENV MKL_THREADING_LAYER=GNU
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# Usage Examples -------------------------------------------------------------------------------------------------------
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# Build and Push
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# t=ultralytics/ultralytics:latest && sudo docker build -f docker/Dockerfile -t $t . && sudo docker push $t
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# Pull and Run
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# t=ultralytics/ultralytics:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t
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# Pull and Run with local directory access
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# t=ultralytics/ultralytics:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t
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# Kill all
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# sudo docker kill $(sudo docker ps -q)
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# Kill all image-based
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# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/ultralytics:latest)
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# DockerHub tag update
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# t=ultralytics/ultralytics:latest tnew=ultralytics/ultralytics:v6.2 && sudo docker pull $t && sudo docker tag $t $tnew && sudo docker push $tnew
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# Clean up
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# sudo docker system prune -a --volumes
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# Update Ubuntu drivers
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# https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/
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# DDP test
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# python -m torch.distributed.run --nproc_per_node 2 --master_port 1 train.py --epochs 3
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# GCP VM from Image
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# docker.io/ultralytics/ultralytics:latest
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