azure-pipelines.docker.yml

# Starter pipeline
# Start with a minimal pipeline that you can customize to build and deploy your code.
# Add steps that build, run tests, deploy, and more:
# https://aka.ms/yaml

pool:
  vmImage: 'Ubuntu 16.04'

steps:
- script: |
    docker pull rasa/rasa_nlu:latest-full
    docker build -t digite/rasa_nlu -f Dockerfile_nlu .
    docker run --rm -v $(Build.ArtifactStagingDirectory)/nlp_service/models:/app/projects digite/rasa_nlu run python -m rasa_nlu.train -c /app/train_model/nlu_config.yml --data /app/train_model/nlu.md -o /app/projects --fixed_model_name nlu --project current --verbose
  displayName: 'train_nlu'

- script: |
    docker pull rasa/rasa_core
    docker build -t digite/rasa_core -f Dockerfile_core .
    docker run --rm -v $(Build.ArtifactStagingDirectory)/nlp_service/models:/app/projects digite/rasa_core run python -m rasa_core.train -d /app/train_model/domain.yml -s /app/train_model/stories.md -o /app/projects/dialogue
  displayName: 'train_dialogue'

- script: |
    docker pull ubuntu:16.04
    docker build -t digite/chat_ui -f Dockerfile_chat .
  displayName: 'build_chat_ui'

- script: |
    docker login -u $(dockerId) -p $(pswd)
    docker push digite/rasa_nlu
    docker push digite/rasa_core
    docker push digite/chat_ui
  displayName: 'publish'