image: python:latest

definitions:
  caches:
    pip-cache: ~/.cache/pip

# Workflow Configuration
pipelines:
  default:
    - step:
        name: Training
        caches:
          - pip-cache
        script:
          - pip install --upgrade --force-reinstall . "awscli>1.20.30"
          - export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
          - export PYTHONUNBUFFERED=TRUE
          - export SAGEMAKER_PROJECT_NAME_ID=$SAGEMAKER_PROJECT_NAME-$SAGEMAKER_PROJECT_ID
          - run-pipeline --module-name pipelines.abalone.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\":\"$SAGEMAKER_PROJECT_NAME_ID\",\"base_job_prefix\":\"$SAGEMAKER_PROJECT_NAME_ID\"}"
          - echo "Create/Update of the SageMaker Pipeline and execution completed."