ci.yaml

name: Build Image

concurrency:
  group: ${{ github.workflow }}-${{ github.ref }}
  cancel-in-progress: true

on:
  pull_request:
  workflow_call:

jobs:
  build:
    strategy:
      fail-fast: false
      max-parallel: 5
      matrix:
        chapter:
         - name: chap1
           folder: Chapter01
         - name: chap2
           folder: Chapter02
         - name: chap3
           folder: Chapter03
         - name: chap4
           folder: Chapter04
         - name: chap5
           folder: Chapter05
         - name: chap6
           folder: Chapter06
         - name: chap7
           folder: Chapter07
         - name: chap8
           folder: Chapter08
         - name: chap9
           folder: Chapter09
         - name: chap10
           folder: Chapter10
         - name: chap12
           folder: Chapter12
    runs-on: ubuntu-22.04
    name: Image ${{ matrix.chapter.name }}
    steps:
      - name: Checkout repository
        uses: actions/checkout@v3

      - name: Extract branch name
        shell: bash
        run: echo "branch=${GITHUB_HEAD_REF:-${GITHUB_REF#refs/heads/}}" >> $GITHUB_OUTPUT
        id: extract_branch

      - name: (GitHub hosted) Free up disk space
        shell: bash
        run: |
          printf '\nDisk usage before cleanup\n'
          df --human-readable
          # Based on https://github.com/actions/runner-images/issues/2840#issuecomment-790492173
          rm -r /usr/share/dotnet
          rm -r /opt/hostedtoolcache/
          printf '\nDisk usage after cleanup\n'
          df --human-readable

      - name: Build Image
        id: build
        run: |
          cd docker
          docker build . --target ${{ matrix.chapter.name }} \
            --build-arg branch=${{ steps.extract_branch.outputs.branch }} \
            -t graph-machine-learning:latest --no-cache 

      - name: Test Image
        id: tests
        env:
          KAGGLE_USERNAME: ${{ secrets.KAGGLE_USERNAME }}
          KAGGLE_TOKEN: ${{ secrets.KAGGLE_TOKEN }}
        run: |

          docker network create my-network

          # Start Neo4j and JanusGraph if we are testing chapter 10
          if [ "${{ matrix.chapter.name }}" == "chap10" ];
          then
            docker run --rm --detach --name janusgraph \
              --publish=8182:8182 \
              janusgraph/janusgraph:1.1.0
            docker network connect my-network janusgraph

            docker run --rm --detach --name neo4j \
              --publish=7474:7474 --publish=7687:7687 \
              --user="$(id -u):$(id -g)" \
              --env NEO4J_AUTH=none \
              --env NEO4J_PLUGINS='["graph-data-science"]' \
              neo4j:5.26.0
            docker network connect my-network neo4j
          fi

          # Start Neo4j if we are testing chapter 13
          if [ "${{ matrix.chapter.name }}" == "chap12" ];
          then
            docker run --rm --detach --name neo4j \
              --publish=7474:7474 --publish=7687:7687 \
              --user="$(id -u):$(id -g)" \
              --env NEO4J_AUTH=none \
              --env NEO4J_PLUGINS='["apoc","apoc-extended"]' \
              --env NEO4J_apoc_export_file_enabled=true \
              --env NEO4J_apoc_import_file_enabled=true \
              --env NEO4J_apoc_import_file_use__neo4j_config=true \
              neo4j:5.26.0
            docker network connect my-network neo4j

            docker run --rm --detach --name ollama \
              --publish=11434:11434 \
              --volume olama:/root/.ollama \
              ollama/ollama:0.6.4
            docker network connect my-network ollama
          fi


          mkdir -p data
          chmod -R 777 data 
          docker run \
            --rm --detach -v "$(pwd)/data:/data" \
            --name graph-machine-learning-box \
            --env KAGGLE_USERNAME=${KAGGLE_USERNAME} \
            --env KAGGLE_KEY=${KAGGLE_TOKEN} \
            --env NEO4J_HOST=neo4j \
            --env JANUSGRAPH_HOST=janusgraph \
            --env OLLAMA_HOST=ollama \
            graph-machine-learning:latest
          docker network connect my-network graph-machine-learning-box

          # Run tests
          cd docker

          ./tests.sh ${{ matrix.chapter.folder }}

      - name: tmate session if tests fail
        if: failure() && github.event_name == 'workflow_dispatch'
        uses: mxschmitt/action-tmate@v3