58 lines
2.7 KiB
Docker
58 lines
2.7 KiB
Docker
# Start from Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
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FROM nvcr.io/nvidia/pytorch:19.08-py3
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# Install dependencies (pip or conda)
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# RUN pip install -U -r requirements.txt
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# RUN conda update -n base -c defaults conda
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# RUN conda install -y -c anaconda future numpy opencv matplotlib tqdm pillow
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# RUN conda install -y -c conda-forge scikit-image tensorboard pycocotools
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# conda install pytorch torchvision -c pytorch
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# Install OpenCV with Gstreamer support
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#WORKDIR /usr/src
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#RUN pip uninstall -y opencv-python
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#RUN apt-get update
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#RUN apt-get install -y gstreamer1.0-python3-dbg-plugin-loader libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev
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#RUN git clone https://github.com/opencv/opencv.git && cd opencv && git checkout 4.1.1 && mkdir build
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#RUN git clone https://github.com/opencv/opencv_contrib.git && cd opencv_contrib && git checkout 4.1.1
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#RUN cd opencv/build && cmake ../ \
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# -D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules \
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# -D BUILD_OPENCV_PYTHON3=ON \
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# -D PYTHON3_EXECUTABLE=/opt/conda/bin/python \
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# -D PYTHON3_INCLUDE_PATH=/opt/conda/include/python3.6m \
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# -D PYTHON3_LIBRARIES=/opt/conda/lib/python3.6/site-packages \
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# -D WITH_GSTREAMER=ON \
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# -D WITH_FFMPEG=ON \
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# && make && make install && ldconfig
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#RUN cd /usr/local/lib/python3.6/site-packages/cv2/python-3.6/ && mv cv2.cpython-36m-x86_64-linux-gnu.so cv2.so
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#RUN cd /opt/conda/lib/python3.6/site-packages/ && ln -s /usr/local/lib/python3.6/site-packages/cv2/python-3.6/cv2.so cv2.so
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#RUN python3 -c "import cv2; print(cv2.getBuildInformation())"
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# Create working directory
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RUN mkdir -p /usr/src/app
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WORKDIR /usr/src/app
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# Copy contents
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COPY . /usr/src/app
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# --------------------------------------------------- Extras Below ---------------------------------------------------
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# Build container
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# rm -rf yolov3 # Warning: remove existing
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# git clone https://github.com/ultralytics/yolov3 && cd yolov3 && python3 detect.py
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# sudo docker image prune -af && sudo docker build -t ultralytics/yolov3:v0 .
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# Run container
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# sudo nvidia-docker run --ipc=host ultralytics/yolov3:v0 python3 detect.py
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# Run container with local directory access
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# sudo nvidia-docker run --ipc=host --mount type=bind,source="$(pwd)"/coco,target=/usr/src/coco ultralytics/yolov3:v0 python3 train.py
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# sudo nvidia-docker run --ipc=host --mount type=bind,source="$(pwd)"/coco,target=/usr/src/coco ultralytics/yolov3:v0 python3 train.py --batch-size 64 --accumulate 1 --img-size 320 --arc uFBCE --prebias --epochs 27
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# Push container to https://hub.docker.com/u/ultralytics
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# docker push ultralytics/yolov3:v0
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# Build and Push
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# export tag=ultralytics/yolov3:v0 && sudo docker build -t $tag . && docker push $tag
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