添加状态分类和液面分割
This commit is contained in:
89
ultralytics_yolov8-main/docker/Dockerfile
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89
ultralytics_yolov8-main/docker/Dockerfile
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# 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:2.3.1-cuda12.1-cudnn8-runtime
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# Set environment variables
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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 PYTHONUNBUFFERED=1 \
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PYTHONDONTWRITEBYTECODE=1 \
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PIP_NO_CACHE_DIR=1 \
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PIP_BREAK_SYSTEM_PACKAGES=1 \
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MKL_THREADING_LAYER=GNU
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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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# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package
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# libsm6 required by libqxcb to create QT-based windows for visualization; set 'QT_DEBUG_PLUGINS=1' to test in docker
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RUN apt update \
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&& apt install --no-install-recommends -y gcc git zip unzip wget curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0 libsm6
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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 /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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ADD https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt .
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# Install pip packages
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RUN python3 -m pip install --upgrade pip wheel
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# Pin TensorRT-cu12==10.1.0 to avoid 10.2.0 bug https://github.com/ultralytics/ultralytics/pull/14239 (note -cu12 must be used)
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RUN pip install -e ".[export]" "tensorrt-cu12==10.1.0" "albumentations>=1.4.6" comet pycocotools
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# Run exports to AutoInstall packages
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# Edge TPU export fails the first time so is run twice here
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RUN yolo export model=tmp/yolov8n.pt format=edgetpu imgsz=32 || 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 https://github.com/PaddlePaddle/X2Paddle/issues/991
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RUN pip install "paddlepaddle>=2.6.0" x2paddle
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# Fix error: `np.bool` was a deprecated alias for the builtin `bool` segmentation error in Tests
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RUN pip install numpy==1.23.5
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# Remove exported models
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RUN rm -rf tmp
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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 with access to all GPUs
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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 access to GPUs 2 and 3 (inside container CUDA devices will appear as 0 and 1)
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# t=ultralytics/ultralytics:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus '"device=2,3"' $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)"/shared/datasets:/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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54
ultralytics_yolov8-main/docker/Dockerfile-arm64
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54
ultralytics_yolov8-main/docker/Dockerfile-arm64
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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# Builds ultralytics/ultralytics:latest-arm64 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
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# Image is aarch64-compatible for Apple M1, M2, M3, Raspberry Pi and other ARM architectures
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# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu with "FROM arm64v8/ubuntu:22.04" (deprecated)
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# Start FROM Debian image for arm64v8 https://hub.docker.com/r/arm64v8/debian (new)
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FROM arm64v8/debian:bookworm-slim
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# Set environment variables
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ENV PYTHONUNBUFFERED=1 \
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PYTHONDONTWRITEBYTECODE=1 \
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PIP_NO_CACHE_DIR=1 \
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PIP_BREAK_SYSTEM_PACKAGES=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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# 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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# pkg-config and libhdf5-dev (not included) are needed to build 'h5py==3.11.0' aarch64 wheel required by 'tensorflow'
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RUN apt update \
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&& apt install --no-install-recommends -y python3-pip git zip unzip wget curl htop gcc libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
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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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ADD https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt .
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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 -e ".[export]"
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# Creates a symbolic link to make 'python' point to 'python3'
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RUN ln -sf /usr/bin/python3 /usr/bin/python
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# Usage Examples -------------------------------------------------------------------------------------------------------
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# Build and Push
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# t=ultralytics/ultralytics:latest-arm64 && sudo docker build --platform linux/arm64 -f docker/Dockerfile-arm64 -t $t . && sudo docker push $t
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# Run
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# t=ultralytics/ultralytics:latest-arm64 && sudo docker run -it --ipc=host $t
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# Pull and Run
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# t=ultralytics/ultralytics:latest-arm64 && 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-arm64 && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/shared/datasets:/datasets $t
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46
ultralytics_yolov8-main/docker/Dockerfile-conda
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46
ultralytics_yolov8-main/docker/Dockerfile-conda
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# 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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# Set environment variables
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ENV PYTHONUNBUFFERED=1 \
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PYTHONDONTWRITEBYTECODE=1 \
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PIP_NO_CACHE_DIR=1 \
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PIP_BREAK_SYSTEM_PACKAGES=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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# 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/v8.2.0/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=12.1 -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=12.1 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)"/shared/datasets:/datasets $t
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60
ultralytics_yolov8-main/docker/Dockerfile-cpu
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60
ultralytics_yolov8-main/docker/Dockerfile-cpu
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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:23.10
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# Set environment variables
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ENV PYTHONUNBUFFERED=1 \
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PYTHONDONTWRITEBYTECODE=1 \
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PIP_NO_CACHE_DIR=1 \
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PIP_BREAK_SYSTEM_PACKAGES=1
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|
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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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# 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 python3-pip git zip unzip wget curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
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||||
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# Create working directory
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WORKDIR /ultralytics
|
||||
|
||||
# Copy contents and configure git
|
||||
COPY . .
|
||||
RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config
|
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ADD https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt .
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||||
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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 -e ".[export]" --extra-index-url https://download.pytorch.org/whl/cpu
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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 https://github.com/PaddlePaddle/X2Paddle/issues/991
|
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# RUN pip install "paddlepaddle>=2.6.0" x2paddle
|
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# Remove exported models
|
||||
RUN rm -rf tmp
|
||||
|
||||
# Creates a symbolic link to make 'python' point to 'python3'
|
||||
RUN ln -sf /usr/bin/python3 /usr/bin/python
|
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|
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|
||||
# Usage Examples -------------------------------------------------------------------------------------------------------
|
||||
|
||||
# Build and Push
|
||||
# t=ultralytics/ultralytics:latest-cpu && sudo docker build -f docker/Dockerfile-cpu -t $t . && sudo docker push $t
|
||||
|
||||
# Run
|
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# t=ultralytics/ultralytics:latest-cpu && sudo docker run -it --ipc=host --name NAME $t
|
||||
|
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# Pull and Run
|
||||
# t=ultralytics/ultralytics:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host --name NAME $t
|
||||
|
||||
# Pull and Run with local volume mounted
|
||||
# t=ultralytics/ultralytics:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/shared/datasets:/datasets $t
|
||||
65
ultralytics_yolov8-main/docker/Dockerfile-jetson-jetpack4
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65
ultralytics_yolov8-main/docker/Dockerfile-jetson-jetpack4
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@ -0,0 +1,65 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# Builds ultralytics/ultralytics:jetson-jetpack4 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
|
||||
# Supports JetPack4.x for YOLOv8 on Jetson Nano, TX2, Xavier NX, AGX Xavier
|
||||
|
||||
# Start FROM https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-cuda
|
||||
FROM nvcr.io/nvidia/l4t-cuda:10.2.460-runtime
|
||||
|
||||
# Set environment variables
|
||||
ENV PYTHONUNBUFFERED=1 \
|
||||
PYTHONDONTWRITEBYTECODE=1
|
||||
|
||||
# Downloads to user config dir
|
||||
ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \
|
||||
https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \
|
||||
/root/.config/Ultralytics/
|
||||
|
||||
# Add NVIDIA repositories for TensorRT dependencies
|
||||
RUN wget -q -O - https://repo.download.nvidia.com/jetson/jetson-ota-public.asc | apt-key add - && \
|
||||
echo "deb https://repo.download.nvidia.com/jetson/common r32.7 main" > /etc/apt/sources.list.d/nvidia-l4t-apt-source.list && \
|
||||
echo "deb https://repo.download.nvidia.com/jetson/t194 r32.7 main" >> /etc/apt/sources.list.d/nvidia-l4t-apt-source.list
|
||||
|
||||
# Install dependencies
|
||||
RUN apt update && \
|
||||
apt install --no-install-recommends -y git python3.8 python3.8-dev python3-pip python3-libnvinfer libopenmpi-dev libopenblas-base libomp-dev gcc
|
||||
|
||||
# Create symbolic links for python3.8 and pip3
|
||||
RUN ln -sf /usr/bin/python3.8 /usr/bin/python3
|
||||
RUN ln -s /usr/bin/pip3 /usr/bin/pip
|
||||
|
||||
# Create working directory
|
||||
WORKDIR /ultralytics
|
||||
|
||||
# Copy contents and configure git
|
||||
COPY . .
|
||||
RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config
|
||||
ADD https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt .
|
||||
|
||||
# Download onnxruntime-gpu 1.8.0 and tensorrt 8.2.0.6
|
||||
# Other versions can be seen in https://elinux.org/Jetson_Zoo and https://forums.developer.nvidia.com/t/pytorch-for-jetson/72048
|
||||
ADD https://nvidia.box.com/shared/static/gjqofg7rkg97z3gc8jeyup6t8n9j8xjw.whl onnxruntime_gpu-1.8.0-cp38-cp38-linux_aarch64.whl
|
||||
ADD https://forums.developer.nvidia.com/uploads/short-url/hASzFOm9YsJx6VVFrDW1g44CMmv.whl tensorrt-8.2.0.6-cp38-none-linux_aarch64.whl
|
||||
|
||||
# Install pip packages
|
||||
RUN python3 -m pip install --upgrade pip wheel
|
||||
RUN pip install \
|
||||
onnxruntime_gpu-1.8.0-cp38-cp38-linux_aarch64.whl \
|
||||
tensorrt-8.2.0.6-cp38-none-linux_aarch64.whl \
|
||||
https://github.com/ultralytics/assets/releases/download/v0.0.0/torch-1.11.0a0+gitbc2c6ed-cp38-cp38-linux_aarch64.whl \
|
||||
https://github.com/ultralytics/assets/releases/download/v0.0.0/torchvision-0.12.0a0+9b5a3fe-cp38-cp38-linux_aarch64.whl
|
||||
RUN pip install -e ".[export]"
|
||||
RUN rm *.whl
|
||||
|
||||
# Usage Examples -------------------------------------------------------------------------------------------------------
|
||||
|
||||
# Build and Push
|
||||
# t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker build --platform linux/arm64 -f docker/Dockerfile-jetson-jetpack4 -t $t . && sudo docker push $t
|
||||
|
||||
# Run
|
||||
# t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker run -it --ipc=host $t
|
||||
|
||||
# Pull and Run
|
||||
# t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker pull $t && sudo docker run -it --ipc=host $t
|
||||
|
||||
# Pull and Run with NVIDIA runtime
|
||||
# t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t
|
||||
59
ultralytics_yolov8-main/docker/Dockerfile-jetson-jetpack5
Normal file
59
ultralytics_yolov8-main/docker/Dockerfile-jetson-jetpack5
Normal file
@ -0,0 +1,59 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# Builds ultralytics/ultralytics:jetson-jetson-jetpack5 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
|
||||
# Supports JetPack5.x for YOLOv8 on Jetson Xavier NX, AGX Xavier, AGX Orin, Orin Nano and Orin NX
|
||||
|
||||
# Start FROM https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-pytorch
|
||||
FROM nvcr.io/nvidia/l4t-pytorch:r35.2.1-pth2.0-py3
|
||||
|
||||
# Set environment variables
|
||||
ENV PYTHONUNBUFFERED=1 \
|
||||
PYTHONDONTWRITEBYTECODE=1 \
|
||||
PIP_NO_CACHE_DIR=1 \
|
||||
PIP_BREAK_SYSTEM_PACKAGES=1
|
||||
|
||||
# Downloads to user config dir
|
||||
ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \
|
||||
https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \
|
||||
/root/.config/Ultralytics/
|
||||
|
||||
# Install linux packages
|
||||
# g++ required to build 'tflite_support' and 'lap' packages
|
||||
# libusb-1.0-0 required for 'tflite_support' package when exporting to TFLite
|
||||
# pkg-config and libhdf5-dev (not included) are needed to build 'h5py==3.11.0' aarch64 wheel required by 'tensorflow'
|
||||
RUN apt update \
|
||||
&& apt install --no-install-recommends -y gcc git zip unzip wget curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
|
||||
|
||||
# Create working directory
|
||||
WORKDIR /ultralytics
|
||||
|
||||
# Copy contents and configure git
|
||||
COPY . .
|
||||
RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config
|
||||
ADD https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt .
|
||||
|
||||
# Remove opencv-python from Ultralytics dependencies as it conflicts with opencv-python installed in base image
|
||||
RUN sed -i '/opencv-python/d' pyproject.toml
|
||||
|
||||
# Download onnxruntime-gpu 1.15.1 for Jetson Linux 35.2.1 (JetPack 5.1). Other versions can be seen in https://elinux.org/Jetson_Zoo#ONNX_Runtime
|
||||
ADD https://nvidia.box.com/shared/static/mvdcltm9ewdy2d5nurkiqorofz1s53ww.whl onnxruntime_gpu-1.15.1-cp38-cp38-linux_aarch64.whl
|
||||
|
||||
# Install pip packages manually for TensorRT compatibility https://github.com/NVIDIA/TensorRT/issues/2567
|
||||
RUN python3 -m pip install --upgrade pip wheel
|
||||
RUN pip install onnxruntime_gpu-1.15.1-cp38-cp38-linux_aarch64.whl
|
||||
RUN pip install -e ".[export]"
|
||||
RUN rm *.whl
|
||||
|
||||
|
||||
# Usage Examples -------------------------------------------------------------------------------------------------------
|
||||
|
||||
# Build and Push
|
||||
# t=ultralytics/ultralytics:latest-jetson-jetpack5 && sudo docker build --platform linux/arm64 -f docker/Dockerfile-jetson-jetpack5 -t $t . && sudo docker push $t
|
||||
|
||||
# Run
|
||||
# t=ultralytics/ultralytics:latest-jetson-jetpack5 && sudo docker run -it --ipc=host $t
|
||||
|
||||
# Pull and Run
|
||||
# t=ultralytics/ultralytics:latest-jetson-jetpack5 && sudo docker pull $t && sudo docker run -it --ipc=host $t
|
||||
|
||||
# Pull and Run with NVIDIA runtime
|
||||
# t=ultralytics/ultralytics:latest-jetson-jetpack5 && sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t
|
||||
55
ultralytics_yolov8-main/docker/Dockerfile-jetson-jetpack6
Normal file
55
ultralytics_yolov8-main/docker/Dockerfile-jetson-jetpack6
Normal file
@ -0,0 +1,55 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# Builds ultralytics/ultralytics:jetson-jetpack6 image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
|
||||
# Supports JetPack6.x for YOLOv8 on Jetson AGX Orin, Orin NX and Orin Nano Series
|
||||
|
||||
# Start FROM https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-jetpack
|
||||
FROM nvcr.io/nvidia/l4t-jetpack:r36.3.0
|
||||
|
||||
# Set environment variables
|
||||
ENV PYTHONUNBUFFERED=1 \
|
||||
PYTHONDONTWRITEBYTECODE=1 \
|
||||
PIP_NO_CACHE_DIR=1 \
|
||||
PIP_BREAK_SYSTEM_PACKAGES=1
|
||||
|
||||
# Downloads to user config dir
|
||||
ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \
|
||||
https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \
|
||||
/root/.config/Ultralytics/
|
||||
|
||||
# Install dependencies
|
||||
RUN apt update && \
|
||||
apt install --no-install-recommends -y git python3-pip libopenmpi-dev libopenblas-base libomp-dev
|
||||
|
||||
# Create working directory
|
||||
WORKDIR /ultralytics
|
||||
|
||||
# Copy contents and configure git
|
||||
COPY . .
|
||||
RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config
|
||||
ADD https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt .
|
||||
|
||||
# Download onnxruntime-gpu 1.18.0 from https://elinux.org/Jetson_Zoo and https://forums.developer.nvidia.com/t/pytorch-for-jetson/72048
|
||||
ADD https://nvidia.box.com/shared/static/48dtuob7meiw6ebgfsfqakc9vse62sg4.whl onnxruntime_gpu-1.18.0-cp310-cp310-linux_aarch64.whl
|
||||
|
||||
# Pip install onnxruntime-gpu, torch, torchvision and ultralytics
|
||||
RUN python3 -m pip install --upgrade pip wheel
|
||||
RUN pip install \
|
||||
onnxruntime_gpu-1.18.0-cp310-cp310-linux_aarch64.whl \
|
||||
https://github.com/ultralytics/assets/releases/download/v0.0.0/torch-2.3.0-cp310-cp310-linux_aarch64.whl \
|
||||
https://github.com/ultralytics/assets/releases/download/v0.0.0/torchvision-0.18.0a0+6043bc2-cp310-cp310-linux_aarch64.whl
|
||||
RUN pip install -e ".[export]"
|
||||
RUN rm *.whl
|
||||
|
||||
# Usage Examples -------------------------------------------------------------------------------------------------------
|
||||
|
||||
# Build and Push
|
||||
# t=ultralytics/ultralytics:latest-jetson-jetpack6 && sudo docker build --platform linux/arm64 -f docker/Dockerfile-jetson-jetpack6 -t $t . && sudo docker push $t
|
||||
|
||||
# Run
|
||||
# t=ultralytics/ultralytics:latest-jetson-jetpack6 && sudo docker run -it --ipc=host $t
|
||||
|
||||
# Pull and Run
|
||||
# t=ultralytics/ultralytics:latest-jetson-jetpack6 && sudo docker pull $t && sudo docker run -it --ipc=host $t
|
||||
|
||||
# Pull and Run with NVIDIA runtime
|
||||
# t=ultralytics/ultralytics:latest-jetson-jetpack6 && sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t
|
||||
57
ultralytics_yolov8-main/docker/Dockerfile-python
Normal file
57
ultralytics_yolov8-main/docker/Dockerfile-python
Normal file
@ -0,0 +1,57 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# Builds ultralytics/ultralytics:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
|
||||
# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv8 deployments
|
||||
|
||||
# Use the official Python 3.10 slim-bookworm as base image
|
||||
FROM python:3.10-slim-bookworm
|
||||
|
||||
# Set environment variables
|
||||
ENV PYTHONUNBUFFERED=1 \
|
||||
PYTHONDONTWRITEBYTECODE=1 \
|
||||
PIP_NO_CACHE_DIR=1 \
|
||||
PIP_BREAK_SYSTEM_PACKAGES=1
|
||||
|
||||
# Downloads to user config dir
|
||||
ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf \
|
||||
https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.Unicode.ttf \
|
||||
/root/.config/Ultralytics/
|
||||
|
||||
# Install linux packages
|
||||
# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package
|
||||
RUN apt update \
|
||||
&& apt install --no-install-recommends -y python3-pip git zip unzip wget curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
|
||||
|
||||
# Create working directory
|
||||
WORKDIR /ultralytics
|
||||
|
||||
# Copy contents and configure git
|
||||
COPY . .
|
||||
RUN sed -i '/^\[http "https:\/\/github\.com\/"\]/,+1d' .git/config
|
||||
ADD https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt .
|
||||
|
||||
# Install pip packages
|
||||
RUN python3 -m pip install --upgrade pip wheel
|
||||
RUN pip install -e ".[export]" --extra-index-url https://download.pytorch.org/whl/cpu
|
||||
|
||||
# Run exports to AutoInstall packages
|
||||
RUN yolo export model=tmp/yolov8n.pt format=edgetpu imgsz=32
|
||||
RUN yolo export model=tmp/yolov8n.pt format=ncnn imgsz=32
|
||||
# Requires Python<=3.10, bug with paddlepaddle==2.5.0 https://github.com/PaddlePaddle/X2Paddle/issues/991
|
||||
RUN pip install "paddlepaddle>=2.6.0" x2paddle
|
||||
# Remove exported models
|
||||
RUN rm -rf tmp
|
||||
|
||||
|
||||
# Usage Examples -------------------------------------------------------------------------------------------------------
|
||||
|
||||
# Build and Push
|
||||
# t=ultralytics/ultralytics:latest-python && sudo docker build -f docker/Dockerfile-python -t $t . && sudo docker push $t
|
||||
|
||||
# Run
|
||||
# t=ultralytics/ultralytics:latest-python && sudo docker run -it --ipc=host $t
|
||||
|
||||
# Pull and Run
|
||||
# t=ultralytics/ultralytics:latest-python && sudo docker pull $t && sudo docker run -it --ipc=host $t
|
||||
|
||||
# Pull and Run with local volume mounted
|
||||
# t=ultralytics/ultralytics:latest-python && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/shared/datasets:/datasets $t
|
||||
45
ultralytics_yolov8-main/docker/Dockerfile-runner
Normal file
45
ultralytics_yolov8-main/docker/Dockerfile-runner
Normal file
@ -0,0 +1,45 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# Builds GitHub actions CI runner image for deployment to DockerHub https://hub.docker.com/r/ultralytics/ultralytics
|
||||
# Image is CUDA-optimized for YOLOv8 single/multi-GPU training and inference tests
|
||||
|
||||
# Start FROM Ultralytics GPU image
|
||||
FROM ultralytics/ultralytics:latest
|
||||
|
||||
# Set environment variables
|
||||
ENV PYTHONUNBUFFERED=1 \
|
||||
PYTHONDONTWRITEBYTECODE=1 \
|
||||
PIP_NO_CACHE_DIR=1 \
|
||||
PIP_BREAK_SYSTEM_PACKAGES=1 \
|
||||
RUNNER_ALLOW_RUNASROOT=1 \
|
||||
DEBIAN_FRONTEND=noninteractive
|
||||
|
||||
# Set the working directory
|
||||
WORKDIR /actions-runner
|
||||
|
||||
# Download and unpack the latest runner from https://github.com/actions/runner
|
||||
RUN FILENAME=actions-runner-linux-x64-2.317.0.tar.gz && \
|
||||
curl -o $FILENAME -L https://github.com/actions/runner/releases/download/v2.317.0/$FILENAME && \
|
||||
tar xzf $FILENAME && \
|
||||
rm $FILENAME
|
||||
|
||||
# Install runner dependencies
|
||||
RUN pip install pytest-cov
|
||||
RUN ./bin/installdependencies.sh && \
|
||||
apt-get -y install libicu-dev
|
||||
|
||||
# Inline ENTRYPOINT command to configure and start runner with default TOKEN and NAME
|
||||
ENTRYPOINT sh -c './config.sh --url https://github.com/ultralytics/ultralytics \
|
||||
--token ${GITHUB_RUNNER_TOKEN:-TOKEN} \
|
||||
--name ${GITHUB_RUNNER_NAME:-NAME} \
|
||||
--labels gpu-latest \
|
||||
--replace && \
|
||||
./run.sh'
|
||||
|
||||
|
||||
# Usage Examples -------------------------------------------------------------------------------------------------------
|
||||
|
||||
# Build and Push
|
||||
# t=ultralytics/ultralytics:latest-runner && sudo docker build -f docker/Dockerfile-runner -t $t . && sudo docker push $t
|
||||
|
||||
# Pull and Run in detached mode with access to GPUs 0 and 1
|
||||
# t=ultralytics/ultralytics:latest-runner && sudo docker run -d -e GITHUB_RUNNER_TOKEN=TOKEN -e GITHUB_RUNNER_NAME=NAME --ipc=host --gpus '"device=0,1"' $t
|
||||
Reference in New Issue
Block a user