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Fix MKL_THREADING_LAYER
bug in DDP Docker training (#3770)
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@ -3,7 +3,7 @@
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# Image is CUDA-optimized for YOLOv8 single/multi-GPU training and inference
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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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# 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:2.0.0-cuda11.7-cudnn8-runtime
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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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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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# Downloads to user config dir
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@ -31,6 +31,8 @@ ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt /u
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# Install pip packages
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# Install pip packages
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RUN python3 -m pip install --upgrade pip wheel
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RUN python3 -m pip install --upgrade pip wheel
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RUN pip install --no-cache -e . albumentations comet thop pycocotools onnx onnx-simplifier onnxruntime-gpu
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RUN pip install --no-cache -e . albumentations comet thop pycocotools onnx onnx-simplifier onnxruntime-gpu
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# Avoid DDP error "MKL_THREADING_LAYER=INTEL is incompatible with libgomp.so.1 library"
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RUN pip install -U numpy
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# Set environment variables
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# Set environment variables
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ENV OMP_NUM_THREADS=1
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ENV OMP_NUM_THREADS=1
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