name: Azure ML Register Model 
description: A Kubeflow pipeline component register model with Azure ML.
inputs:
- {name: base_path, type: String}
- {name: model_file, type: String}
- {name: model_name, type: String}
- {name: tenant_id, type: String}
- {name: service_principal_id, type: String}
- {name: service_principal_password, type: String}
- {name: subscription_id, type: String}
- {name: resource_group, type: String}
- {name: workspace, type: String}
- {name: run_id, type: String}
outputs:
    - {name: output_model_path, type: String, description: 'Name of registered model'}
implementation:
  container:
    image: ''
    command: [
      "python", "/scripts/register.py",
      '--base_path', {inputValue: base_path},
      '--model', {inputValue: model_file},
      '--model_name', {inputValue: model_name},
      '--tenant_id', {inputValue: tenant_id},
      '--service_principal_id', {inputValue: service_principal_id},
      '--service_principal_password', {inputValue: service_principal_password},
      '--subscription_id', {inputValue: subscription_id},
      '--resource_group', {inputValue: resource_group},
      '--workspace', {inputValue: workspace},
      '--run_id', {inputValue: run_id},
      '--output_model_path',{outputPath: output_model_path} 
    ]
