version: 0.2
artifacts:
  files:
    - /tmp/ml/**/*
  name: ml

phases:
  install:
    runtime-versions:
      python: 3.8
    commands:
      - pip install --upgrade .

  pre_build:
    commands:
      - aws s3 sync $CODEBUILD_SRC_DIR_datasets s3://${ARTIFACT_BUCKET}/train/datasets/ESC-50/  --include="*.wav" --include="*.csv"
  build:
    commands:
      - export PYTHONUNBUFFERED=TRUE
      - export SAGEMAKER_PROJECT_NAME_ID="${SAGEMAKER_PROJECT_NAME}-${SAGEMAKER_PROJECT_ID}"
      - |
        run-pipeline --module-name pipelines.stm.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}\",\"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\":\"${SAGEMAKER_PROJECT_NAME_ID}\",\"sagemaker_project_name\":\"${SAGEMAKER_PROJECT_NAME}\"}"
      - echo "Create/Update of the SageMaker Pipeline and execution completed."
      - aws s3 cp s3://${ARTIFACT_BUCKET}/evaluation/build/ /tmp/ml --recursive