ci-cd.yml

####### uncomment below lines to use CML without AWS credentials
####### add ci-cd steps here

# name: train-NLP-ML-usecase

# on: [push]

# jobs:
#   run:
#     runs-on: [ubuntu-latest]
#     steps:
#       - uses: actions/checkout@v2
#       - uses: iterative/setup-cml@v1
#       - uses: actions/setup-python@v2
#         with:
#           python-version: '3.7'

      
#       - name: Update Node.js to v16
#         run: sudo apt-get install -y nodejs npm

#       - name: cml_run & Install Dependencies and Reproduce DVC

#         run: |
#           # Your ML workflow commands
#           pip install --upgrade pip
#           pip install -r requirements.txt
#           dvc repro --force -v                        ## v >>> verbose
#           echo "# REPORTS" >> report.md
#           echo "## metrics" >> report.md
#           cat scores.json >> report.md
         
    
#       - name: Comment on Last Commit
#         run: cml comment create report.md ## >> CML COMMAND USE TO COMMENT ON LAST COMMIT   
#         env:
#           repo_token: ${{secrets.GITHUB_TOKEN}}


###uncomment below lines to use CML with AWS credentials


name: train-NLP-ML-usecase

on: [push]

jobs:
  deploy-runner:
    runs-on: [ubuntu-latest]
    steps:
      - uses: iterative/setup-cml@v1
      - uses: actions/checkout@v2
      - name: 'Deploy runner in EC2'
        shell: bash
        env:
          REPO_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
          AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
          AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
        run: |
          cml-runner \
          --cloud aws \
          --cloud-region us-west \
          --cloud-type=m \              ### above line is for  medium size data .we may incure some chagrges from AWS (we can use t2.macro but run may fail )
          --labels=cml-runner 


  model-training:
    needs: deploy-runner
    runs-on: [self-hosted, cml-runner]
    container: docker://iterativeai/cml:0-dvc2-base1
    steps:
      - uses: actions/checkout@v2
      - name: "Train my model"
        env:
          repo_token: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
        run: |
          # Your ML workflow commands
          pip install --upgrade pip
          pip install -r requirements.txt

          dvc repro -v ## v >>> verbose

          echo "# REPORTS" >> report.md
          echo "## metrics" >> report.md
          cat scores.json >> report.md
          cml comment create report.md # >> CML COMMAND USE TO COMMENT ON LAST COMMIT