version: 0.2

phases:
  install:
    runtime-versions:
      python: 3.8
    commands:
      - pip install -U . "sagemaker"
  
  build:
    commands:
      - export PYTHONUNBUFFERED=TRUE
      - export SAGEMAKER_PROJECT_NAME_ID="${SAGEMAKER_PROJECT_NAME}-${SAGEMAKER_PROJECT_ID}"
      - |
        run-pipeline --module-name pipelines.nlp.pipeline \
          --role-arn $SAGEMAKER_PIPELINE_ROLE_ARN \
          --tags "[{\"Key\":\"sagemaker:project-name\", \"Value\":\"${SAGEMAKER_PROJECT_NAME}\"}, {\"Key\":\"sagemaker:project-id\", \"Value\":\"${SAGEMAKER_PROJECT_ID}\"}]" \
          --kwargs "{\"region\":\"${AWS_REGION}\",\"sagemaker_project_arn\":\"${SAGEMAKER_PROJECT_ARN}\",\"role\":\"${SAGEMAKER_PIPELINE_ROLE_ARN}\",\"default_bucket\":\"${ARTIFACT_BUCKET}\",\"pipeline_name\":\"${SAGEMAKER_PROJECT_NAME_ID}\",\"model_package_group_name_1\":\"${SAGEMAKER_PROJECT_NAME_ID}-1\",\"model_package_group_name_2\":\"${SAGEMAKER_PROJECT_NAME_ID}-2\",\"base_job_prefix\":\"${SAGEMAKER_PROJECT_NAME_ID}\"}"
      - echo "Create/Update of the SageMaker Pipeline and execution completed."