# 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/v8.2.0/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=12.1 -c pytorch -c nvidia && \
    conda install -c conda-forge ultralytics mkl
    # conda install -c pytorch -c nvidia -c conda-forge pytorch torchvision pytorch-cuda=12.1 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)"/shared/datasets:/usr/src/datasets $t