Inhalt der JSON-Datei als Python-Wörterverzeichnis laden

Für das vorliegende Beispiel definiert die Datei sagemaker_native_multiclass_breast-cancer_all_monitors_sub_configuration.json Konfigurationsdaten für ein Modell, das den Typ von Krebs vorhersagt.

Führen Sie den folgenden Befehl aus, um die Datei in Python zu laden:

configuration_file_path = 'sagemaker_native_multiclass_breast-cancer_all_monitors_sub_configuration.json'

  with open(configuration_file_path, 'r') as fp:
subscription_configuration = json.load(fp)

Die Datei enthält Konfigurationsdaten. Beachten Sie das folgende Beispiel. Ein vollständiges Beispiel für den Konfigurationsinhalt enthält das Notebook.

{'asset': {'asset_id': '0530ab0cd4f4dd5486b19c08df8b6914',
'asset_type': 'model',
'created_at': '2018-10-10T14:31:44.348Z',
'name': 'DEMO-multi-classification-2018-10-10-14-26-26',
'url': 's3://sagemaker-us-east-1-014862798213/sagemaker/DEMO-breast-cancer-prediction/DEMO-multi-classification-2018-10-10-14-26-26/output/model.tar.gz'},
'asset_properties': {'categorical_fields': [],
'feature_fields': ['radius_mean',
  'texture_mean',
  . . .

'input_data_schema': {'fields': [{'metadata': {'modeling_role': 'feature'},
    'name': 'radius_mean',
    'nullable': True,
    'type': 'double'},
  {'metadata': {'modeling_role': 'feature'},
    'name': 'texture_mean',
    'nullable': True,
    'type': 'double'},
  . . .

'input_data_type': 'structured',
'label_column': 'diagnosis',
'output_data_schema': {'fields': [{'metadata': {'modeling_role': 'feature'},
    'name': 'radius_mean',
    'nullable': True,
    'type': 'double'},
  {'metadata': {'modeling_role': 'feature'},
    'name': 'texture_mean',
    'nullable': True,
    'type': 'double'},
  . . .

'prediction_field': 'predicted_label',
'prediction_probability_field': 'score',
'problem_type': 'multiclass',
'training_data_schema': {'fields': [{'metadata': {'modeling_role': 'feature'},
    'name': 'radius_mean',
    'nullable': True,
    'type': 'double'},
  {'metadata': {'modeling_role': 'feature'},
    'name': 'texture_mean',
    'nullable': True,
    'type': 'double'},
  . . .

'configurations': {'explainability': {'training_statistics': {'base_values': {'0': 13.37,
    '1': 18.84,
    '10': 0.3242,
  . . .

'fairness_monitoring': {'class_label': 'predicted_label',
  'distributions': [{'attribute': 'radius_mean',
    'class_labels': [{'counts': [{'class_value': 'B', 'count': 1}],
      'label': '[6.8, 7.2]'},
    {'counts': [{'class_value': 'B', 'count': 3}], 'label': '[7.6, 8.0]'},
    {'counts': [{'class_value': 'B', 'count': 2}], 'label': '[8.0, 8.4]'},
  . . .

  'favourable_class': ['M'],
  'features': [{'feature': 'radius_mean',
    'majority': [[0.0, 10.0], [19.0, 20.0]],
    'minority': [[15.0, 16.0]],
    'threshold': 0.8,
    'type': 'float'}],
  'min_records': 5,
  'perform_debias': True,
  'run_status': 'INITIATED',
  'training_data_class_label': None,
  'unfavourable_class': ['B']},
'payload_logging': {'dynamic_schema_update': True,
  'output_data_schema': {'fields': [{'metadata': {'modeling_role': 'feature'},
    'name': 'radius_mean',
    'nullable': True,
    'type': 'double'},
    {'metadata': {'modeling_role': 'feature'},
    'name': 'texture_mean',
    'nullable': True,
    'type': 'double'},
  . . .

'performance_monitoring': {},
'quality_monitoring': {'evaluation_definition': {'method': 'multiclass',
  'threshold': 0.8},
  'min_feedback_data_size': 5,
  'scheduleId': '63c7f400-aa29-4539-91ad-8a4b9d2b9a51'}},
'deployments': [{'created_at': '2018-10-10T14:39:21.421Z',
  'deployment_id': '37a83f399e6dc3b9d08d7d01fe690665',
  'deployment_rn': 'arn:aws:sagemaker:us-east-1:014862798213:endpoint/demo-multi-classification-endpoint-201810101439',
  'deployment_type': 'online',
  'name': 'DEMO-multi-classification-endpoint-201810101439',
  'scoring_endpoint': {'request_headers': {'Content-Type': 'application/json'},
  'url': 'DEMO-multi-classification-endpoint-201810101439'},
  'url': 'DEMO-multi-classification-endpoint-201810101439'}],
'export_info': {'api_version': 'v1',
'origin': '/v1/data_marts/b73545e6-0a6e-466c-8cd0-c47c044c5702/service_bindings/bf44cc7f-990d-4942-bfc6-cbcf71a1b78c/subscriptions/0530ab0cd4f4dd5486b19c08df8b6914',
'timestamp': '2019-02-11T11:41:01.613Z'}}

Aus Konfigurationsdatei importieren

Führen Sie nun den Aufruf aus, um die Assetbereitstellung des Vorhersagebeispielmodells für Brustkrebs hinzuzufügen und zu konfigurieren.

subscription = client.data_mart.subscriptions.import_configuration(binding_uid=binding_uid, configuration_data=subscription_configuration)

Der Parameter binding_uid ist optional, wenn nur eine ML-Engine gebunden wird.

In die Konfigurationsdatei exportieren

Sie können die Konfigurationsdatei auch als JSON-Datei exportieren:

exported_configuration = client.data_mart.subscriptions.export_configuration(binding_uid=binding_uid, subscription_uid=subscription.uid)

Ergebnisse

Das Asset Deployment ist erstellt und kann für Modellevaluierungen verwendet werden.

Übergeordnetes Thema: Modellauswertung vorbereiten