Glenn Jocher
34cf749958
* Update LICENSE to AGPL-3.0 This pull request updates the license of the YOLOv5 project from GNU General Public License v3.0 (GPL-3.0) to GNU Affero General Public License v3.0 (AGPL-3.0). We at Ultralytics have decided to make this change in order to better protect our intellectual property and ensure that any modifications made to the YOLOv5 source code will be shared back with the community when used over a network. AGPL-3.0 is very similar to GPL-3.0, but with an additional clause to address the use of software over a network. This change ensures that if someone modifies YOLOv5 and provides it as a service over a network (e.g., through a web application or API), they must also make the source code of their modified version available to users of the service. This update includes the following changes: - Replace the `LICENSE` file with the AGPL-3.0 license text - Update the license reference in the `README.md` file - Update the license headers in source code files We believe that this change will promote a more collaborative environment and help drive further innovation within the YOLOv5 community. Please review the changes and let us know if you have any questions or concerns. Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update headers to AGPL-3.0 --------- Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
75 lines
2.6 KiB
Docker
75 lines
2.6 KiB
Docker
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
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# Builds ultralytics/yolov5:latest image on DockerHub https://hub.docker.com/r/ultralytics/yolov5
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# Image is CUDA-optimized for YOLOv5 single/multi-GPU training and inference
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# Start FROM PyTorch image https://hub.docker.com/r/pytorch/pytorch
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FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-runtime
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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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ENV DEBIAN_FRONTEND noninteractive
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RUN apt update
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RUN TZ=Etc/UTC apt install -y tzdata
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RUN apt install --no-install-recommends -y gcc git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg
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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
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# Create working directory
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RUN rm -rf /usr/src/app && 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 (issues as not a .git directory)
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RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app
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# Install pip packages
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COPY requirements.txt .
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RUN python3 -m pip install --upgrade pip wheel
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RUN pip install --no-cache -r requirements.txt albumentations comet gsutil notebook \
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coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2022.3'
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# tensorflow tensorflowjs \
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# Set environment variables
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ENV OMP_NUM_THREADS=1
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# Cleanup
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ENV DEBIAN_FRONTEND teletype
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# Usage Examples -------------------------------------------------------------------------------------------------------
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# Build and Push
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# t=ultralytics/yolov5:latest && sudo docker build -f utils/docker/Dockerfile -t $t . && sudo docker push $t
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# Pull and Run
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# t=ultralytics/yolov5: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/yolov5: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/yolov5:latest)
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# DockerHub tag update
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# t=ultralytics/yolov5:latest tnew=ultralytics/yolov5: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/yolov5:latest
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