示例#1
0
    def test_success_create_version(self):
        with patch('airflow.gcp.operators.mlengine.MLEngineHook') \
                as mock_hook:
            success_response = {'name': 'some-name', 'done': True}
            hook_instance = mock_hook.return_value
            hook_instance.create_version.return_value = success_response

            training_op = MLEngineVersionOperator(version=self.VERSION_INPUT,
                                                  **self.VERSION_DEFAULT_ARGS)
            training_op.execute(None)

            mock_hook.assert_called_once_with(
                gcp_conn_id='google_cloud_default', delegate_to=None)
            # Make sure only 'create_version' is invoked on hook instance
            self.assertEqual(len(hook_instance.mock_calls), 1)
            hook_instance.create_version.assert_called_once_with(
                'test-project', 'test-model', self.VERSION_INPUT)
    def test_success_create_version(self, mock_hook):
        success_response = {'name': 'some-name', 'done': True}
        hook_instance = mock_hook.return_value
        hook_instance.create_version.return_value = success_response

        training_op = MLEngineVersionOperator(version=TEST_VERSION,
                                              **self.VERSION_DEFAULT_ARGS)
        training_op.execute(None)

        mock_hook.assert_called_once_with(gcp_conn_id='google_cloud_default',
                                          delegate_to=None)
        # Make sure only 'create_version' is invoked on hook instance
        self.assertEqual(len(hook_instance.mock_calls), 1)
        hook_instance.create_version.assert_called_once_with(
            project_id='test-project',
            model_name='test-model',
            version_spec=TEST_VERSION)
示例#3
0
                                      model={
                                          "name": MODEL_NAME,
                                      })

    get_model_result = BashOperator(
        bash_command="echo \"{{ task_instance.xcom_pull('get-model') }}\"",
        task_id="get-model-result",
    )

    create_version = MLEngineVersionOperator(
        task_id="create-version",
        project_id=PROJECT_ID,
        model_name=MODEL_NAME,
        operation='create',
        version={
            "name": "v1",
            "description": "First-version",
            "deployment_uri": '{}/keras_export/'.format(JOB_DIR),
            "runtime_version": "1.14",
            "machineType": "mls1-c1-m2",
            "framework": "TENSORFLOW",
            "pythonVersion": "3.5"
        })

    create_version_2 = MLEngineVersionOperator(task_id="create-version-2",
                                               project_id=PROJECT_ID,
                                               model_name=MODEL_NAME,
                                               operation='create',
                                               version={
                                                   "name": "v2",
                                                   "description":
                                                   "Second version",