# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

# Builds GitHub actions CI runner image for deployment to DockerHub https://hub.docker.com/r/ultralytics/ultralytics
# Image is CUDA-optimized for YOLO single/multi-GPU training and inference tests

# Start FROM Ultralytics GPU image
FROM ultralytics/ultralytics:latest

# Set additional environment variables for runner
ARG RUNNER_VERSION=2.328.0
ENV RUNNER_ALLOW_RUNASROOT=1 \
    DEBIAN_FRONTEND=noninteractive

# Set the working directory
WORKDIR /actions-runner

# Download and unpack the latest runner from https://github.com/actions/runner and install dependencies
RUN FILENAME=actions-runner-linux-x64-${RUNNER_VERSION}.tar.gz && \
    curl -o "$FILENAME" -L "https://github.com/actions/runner/releases/download/v${RUNNER_VERSION}/${FILENAME}" && \
    tar xzf "$FILENAME" && \
    rm "$FILENAME" && \
    # Install runner dependencies \
    uv pip install --system pytest-cov && \
    ./bin/installdependencies.sh && \
    apt-get -y install libicu-dev

# JSON ENTRYPOINT command to configure and start runner with default TOKEN and NAME
ENTRYPOINT ["sh", "-c", "./config.sh --url https://github.com/ultralytics/ultralytics --token ${GITHUB_RUNNER_TOKEN:-TOKEN} --name ${GITHUB_RUNNER_NAME:-NAME} --labels gpu-latest --replace && ./run.sh"]

# Usage Examples -------------------------------------------------------------------------------------------------------

# Build and Push
# t=ultralytics/ultralytics:latest-runner && sudo docker build -f docker/Dockerfile-runner -t $t . && sudo docker push $t

# Pull and Run in detached mode with access to GPUs 0 and 1
# t=ultralytics/ultralytics:latest-runner && sudo docker run -d -e GITHUB_RUNNER_TOKEN=TOKEN -e GITHUB_RUNNER_NAME=NAME --ipc=host --gpus '"device=0,1"' $t