| # Ultralytics YOLO 🚀, AGPL-3.0 license | |
| # Builds ultralytics/ultralytics:latest-conda image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics | |
| # Image is optimized for Ultralytics Anaconda (https://anaconda.org/conda-forge/ultralytics) installation and usage | |
| # Start FROM miniconda3 image https://hub.docker.com/r/continuumio/miniconda3 | |
| FROM continuumio/miniconda3:latest | |
| # 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 | |
| RUN apt update \ | |
| && apt install --no-install-recommends -y libgl1 | |
| # Copy contents | |
| ADD https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8n.pt . | |
| # Install conda packages | |
| # mkl required to fix 'OSError: libmkl_intel_lp64.so.2: cannot open shared object file: No such file or directory' | |
| RUN conda config --set solver libmamba && \ | |
| conda install pytorch torchvision pytorch-cuda=11.8 -c pytorch -c nvidia && \ | |
| conda install -c conda-forge ultralytics mkl | |
| # conda install -c pytorch -c nvidia -c conda-forge pytorch torchvision pytorch-cuda=11.8 ultralytics mkl | |
| # Usage Examples ------------------------------------------------------------------------------------------------------- | |
| # Build and Push | |
| # t=ultralytics/ultralytics:latest-conda && sudo docker build -f docker/Dockerfile-cpu -t $t . && sudo docker push $t | |
| # Run | |
| # t=ultralytics/ultralytics:latest-conda && sudo docker run -it --ipc=host $t | |
| # Pull and Run | |
| # t=ultralytics/ultralytics:latest-conda && sudo docker pull $t && sudo docker run -it --ipc=host $t | |
| # Pull and Run with local volume mounted | |
| # t=ultralytics/ultralytics:latest-conda && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t | |