text: import os
  
  TEMP_TAR_GZ_FILENAME = '/tmp/fairing.layer.tar.gz'
  DEFAULT_IMAGE_NAME = 'fairing-job'
  DEFAULT_BASE_IMAGE = 'gcr.io/kubeflow-images-public/fairing:dev'
  DEFAULT_REGISTRY = 'index.docker.io'
  DEFAULT_DEST_PREFIX = '/app/'
  
  DEFAULT_CONTEXT_FILENAME = '/tmp/fairing.context.tar.gz'
  DEFAULT_GENERATED_DOCKERFILE_FILENAME = '/tmp/Dockerfile'
  
  GOOGLE_CREDS_ENV = 'GOOGLE_APPLICATION_CREDENTIALS'
  GCP_SERVICE_ACCOUNT_NAME = 'default-editor'
  
  GCP_CREDS_SECRET_NAME = 'user-gcp-sa'
  AWS_CREDS_SECRET_NAME = 'aws-secret'
  DOCKER_CREDS_SECRET_NAME = "docker-secret"
  
  # IBM Cloud Constants
  IBM_COS_CREDS_FILE_NAME = 'cos_credentials'
  IBM_COS_CREDS_SECRET_NAME = 'ibm-cos-creds'
  IBM_COS_DEFAULT_ENDPOINT = 'https://s3.us.cloud-object-storage.appdomain.cloud'
  
  # See https://github.com/kubeflow/website/issues/1033 for documentation about these secrets.
  AZURE_CREDS_SECRET_NAME = 'azcreds'
  AZURE_ACR_CREDS_SECRET_NAME = 'acrcreds'
  # The secret containing credentials to access a specific storage account is dynamically generated
  # by using Azure credentials to get those storage credentials.
  AZURE_STORAGE_CREDS_SECRET_NAME_PREFIX = 'storage-credentials-'
  AZURE_FILES_SHARED_FOLDER = 'fairing-builds'
  
  DEFAULT_USER_AGENT = 'kubeflow-fairing/{VERSION}'
  
  # Job Constants
  JOB_DEFAULT_NAME = 'fairing-job-'
  JOB_DEPLOPYER_TYPE = 'job'
  
  # Serving Constants
  SERVING_DEPLOPYER_TYPE = 'serving'
  
  #TFJob Constants
  TF_JOB_VERSION = os.environ.get('TF_JOB_VERSION', 'v1')
  TF_JOB_GROUP = "kubeflow.org"
  TF_JOB_KIND = "TFJob"
  TF_JOB_PLURAL = "tfjobs"
  TF_JOB_DEFAULT_NAME = 'fairing-tfjob-'
  TF_JOB_DEPLOYER_TYPE = 'tfjob'
  
  #PyTorchJob Constants
  PYTORCH_JOB_VERSION = os.environ.get('PYTORCH_JOB_VERSION', 'v1')
  PYTORCH_JOB_GROUP = "kubeflow.org"
  PYTORCH_JOB_KIND = "PyTorchJob"
  PYTORCH_JOB_PLURAL = "pytorchjobs"
  PYTORCH_JOB_DEFAULT_NAME = 'fairing-pytorchjob-'
  PYTORCH_JOB_DEPLOYER_TYPE = 'pytorchjob'
  
  # KFServing constants
  KFSERVING_GROUP = "serving.kubeflow.org"
  KFSERVING_KIND = "InferenceService"
  KFSERVING_PLURAL = "inferenceservices"
  KFSERVING_VERSION = 'v1alpha2'
  KFSERVING_DEFAULT_NAME = 'fairing-kfserving-'
  KFSERVING_DEPLOYER_TYPE = 'kfservice'
  KFSERVING_CONTAINER_NAME = 'user-container'
  
  # persistent volume claim constants
  PVC_DEFAULT_MOUNT_PATH = '/mnt'
  PVC_DEFAULT_VOLUME_NAME = 'fairing-volume-'
  
  # Kaniko Constants
  KANIKO_IMAGE = 'gcr.io/kaniko-project/executor:v0.14.0'
  
  #Fairing Logging Constants
  FAIRING_LOG_LEVEL = os.environ.get('FAIRING_LOG_LEVEL', 'INFO').upper()
  FAIRING_LOG_FORMAT = '%(levelname)s|%(asctime)s|%(pathname)s|%(lineno)d| %(message)s'
  FAIRING_LOG_DATEFMT = '%Y-%m-%d %H:%M:%S'
  