03-train-evaluate-register-model.yml

# Continuous Integration (CI) pipeline that orchestrates the training, evaluation, and registration of the model.
parameters:
  - name: aml_pipeline_name
    displayName: AML Pipeline Name to trigger
    type: string
    default: flower-preprocessing-pipeline
  - name: workingDirectory
    displayName: Working Directory for sample
    type: string
    default: samples/image-classification-tensorflow

resources:
  containers:
  - container: mlops
    image: mlopsmanufacturing/build-agent/tensorflow:latest
    endpoint: mlopsmanufacturingacr
  pipelines:
  - pipeline: preprocess-ci
    source: 02-preprocess-data # Name of the triggering pipeline
    trigger:
      branches:
        include:
        - main

pr: none
trigger:
  branches:
    include:
    - main
  paths:
    include:
    - samples/image-classification-tensorflow/ml_model/train/
    - samples/image-classification-tensorflow/ml_model/evaluate/
    - samples/image-classification-tensorflow/ml_model/register/
    - samples/image-classification-tensorflow/ml_model/util/
    - samples/image-classification-tensorflow/ml_model/conda_dependencies.yml
    - samples/image-classification-tensorflow/ml_model/parameters.json
    - samples/image-classification-tensorflow/ml_service/pipelines/build_training_pipeline.py
    - samples/image-classification-tensorflow/devops_pipelines/03-train-evaluate-register-model.yml
    - samples/image-classification-tensorflow/devops_pipelines/get-model-id-artifact-template.yml
    - samples/image-classification-tensorflow/devops_pipelines/publish-model-artifact-template.yml
    - samples/image-classification-tensorflow/devops_pipelines/variables-template.yml
    - samples/image-classification-tensorflow/devops_pipelines/code-quality-template.yml


variables:
- template: variables-template.yml
- group: devopsforai-aml-vg

pool:
  vmImage: ubuntu-latest

stages:
- stage: 'Model_CI'
  displayName: 'Model CI'
  jobs:
  - job: "Model_CI_Pipeline"
    displayName: "Model CI Pipeline"
    container: mlops
    timeoutInMinutes: 0
    steps:
    - template: code-quality-template.yml
      parameters:
        workingDirectory: ${{ parameters.workingDirectory }}
        targetDirectory: 'ml_model'
    - task: AzureCLI@1
      inputs:
        azureSubscription: '$(WORKSPACE_SVC_CONNECTION)'
        scriptLocation: inlineScript
        workingDirectory: ${{ parameters.workingDirectory }}
        inlineScript: |
          set -e # fail on error
          export SUBSCRIPTION_ID=$(az account show --query id -o tsv)
          # Invoke the Python building and publishing a training pipeline
          python -m ml_service.pipelines.build_training_pipeline
      displayName: 'Publish Azure Machine Learning Pipeline'

- stage: 'Trigger_Training_Pipeline'
  displayName: 'Train and evaluate model'
  condition: succeeded()
  variables:
    BUILD_URI: '$(SYSTEM.COLLECTIONURI)$(SYSTEM.TEAMPROJECT)/_build/results?buildId=$(BUILD.BUILDID)'
  jobs:
  - job: "Get_Pipeline_ID"
    condition: and(succeeded(), eq(coalesce(variables['auto-trigger-training'], 'true'), 'true'))
    displayName: "Get Pipeline ID for execution"
    container: mlops
    timeoutInMinutes: 0
    steps:
    - task: AzureCLI@1
      inputs:
        azureSubscription: '$(WORKSPACE_SVC_CONNECTION)'
        scriptLocation: inlineScript
        workingDirectory: ${{ parameters.workingDirectory }}
        inlineScript: |
          set -e # fail on error
          export SUBSCRIPTION_ID=$(az account show --query id -o tsv)
          python -m ml_service.pipelines.run_training_pipeline --output_pipeline_id_file "pipeline_id.txt" --skip_train_execution
          # Set AMLPIPELINEID variable for next AML Pipeline task in next job
          AMLPIPELINEID="$(cat pipeline_id.txt)"
          echo "##vso[task.setvariable variable=AMLPIPELINEID;isOutput=true]$AMLPIPELINEID"
      name: 'getpipelineid'
      displayName: 'Get Pipeline ID'

  - job: "Run_ML_Pipeline"
    dependsOn: "Get_Pipeline_ID"
    displayName: "Trigger ML Training Pipeline"
    timeoutInMinutes: 0
    pool: server
    variables:
      AMLPIPELINE_ID: $[ dependencies.Get_Pipeline_ID.outputs['getpipelineid.AMLPIPELINEID'] ]
    steps:
    - task: ms-air-aiagility.vss-services-azureml.azureml-restApi-task.MLPublishedPipelineRestAPITask@0
      displayName: 'Invoke ML pipeline'
      inputs:
        azureSubscription: '$(WORKSPACE_SVC_CONNECTION)'
        PipelineId: '$(AMLPIPELINE_ID)'
        ExperimentName: '$(EXPERIMENT_NAME)'
        PipelineParameters: '"ParameterAssignments": {"model_name": "$(MODEL_NAME)"}, "tags": {"BuildId": "$(Build.BuildId)", "BuildUri": "$(BUILD_URI)"}, "StepTags": {"BuildId": "$(Build.BuildId)", "BuildUri": "$(BUILD_URI)"}'
  
  - job: "Training_Run_Report"
    dependsOn: "Run_ML_Pipeline"
    container: mlops
    condition: always()
    displayName: "Publish artifact if new model was registered"
    timeoutInMinutes: 0
    steps:
    - template: publish-model-artifact-template.yml
      parameters:
        workingDirectory: ${{ parameters.workingDirectory }}