def test_get_expected_model_with_framework_estimator(tensorflow_estimator):
    training_step = TrainingStep('Training',
                                 estimator=tensorflow_estimator,
                                 data={'train': 's3://sagemaker/train'},
                                 job_name='tensorflow-job',
                                 mini_batch_size=1024)
    expected_model = training_step.get_expected_model()
    expected_model.entry_point = 'tf_train.py'
    model_step = ModelStep('Create model',
                           model=expected_model,
                           model_name='tf-model')
    assert model_step.to_dict() == {
        'Type': 'Task',
        'Parameters': {
            'ExecutionRoleArn': EXECUTION_ROLE,
            'ModelName': 'tf-model',
            'PrimaryContainer': {
                'Environment': {
                    'SAGEMAKER_PROGRAM': 'tf_train.py',
                    'SAGEMAKER_SUBMIT_DIRECTORY':
                    's3://sagemaker/tensorflow-job/source/sourcedir.tar.gz',
                    'SAGEMAKER_CONTAINER_LOG_LEVEL': '20',
                    'SAGEMAKER_REGION': 'us-east-1',
                },
                'Image': expected_model.image_uri,
                'ModelDataUrl.$': "$['ModelArtifacts']['S3ModelArtifacts']"
            }
        },
        'Resource': 'arn:aws:states:::sagemaker:createModel',
        'End': True
    }
def test_training_step_creation_with_model(pca_estimator):
    training_step = TrainingStep('Training',
                                 estimator=pca_estimator,
                                 job_name='TrainingJob')
    model_step = ModelStep(
        'Training - Save Model',
        training_step.get_expected_model(
            model_name=training_step.output()['TrainingJobName']))
    training_step.next(model_step)
    assert training_step.to_dict() == {
        'Type': 'Task',
        'Parameters': {
            'AlgorithmSpecification': {
                'TrainingImage': PCA_IMAGE,
                'TrainingInputMode': 'File'
            },
            'OutputDataConfig': {
                'S3OutputPath': 's3://sagemaker/models'
            },
            'StoppingCondition': {
                'MaxRuntimeInSeconds': 86400
            },
            'ResourceConfig': {
                'InstanceCount': 1,
                'InstanceType': 'ml.c4.xlarge',
                'VolumeSizeInGB': 30
            },
            'RoleArn': EXECUTION_ROLE,
            'HyperParameters': {
                'feature_dim': '50000',
                'num_components': '10',
                'subtract_mean': 'True',
                'algorithm_mode': 'randomized',
                'mini_batch_size': '200'
            },
            'TrainingJobName': 'TrainingJob'
        },
        'Resource': 'arn:aws:states:::sagemaker:createTrainingJob.sync',
        'Next': 'Training - Save Model'
    }

    assert model_step.to_dict() == {
        'Type': 'Task',
        'Resource': 'arn:aws:states:::sagemaker:createModel',
        'Parameters': {
            'ExecutionRoleArn': EXECUTION_ROLE,
            'ModelName.$': "$['TrainingJobName']",
            'PrimaryContainer': {
                'Environment': {},
                'Image': PCA_IMAGE,
                'ModelDataUrl.$': "$['ModelArtifacts']['S3ModelArtifacts']"
            }
        },
        'End': True
    }
def test_model_step_creation(pca_model):
    step = ModelStep('Create model', model=pca_model, model_name='pca-model')
    assert step.to_dict() == {
        'Type': 'Task',
        'Parameters': {
            'ExecutionRoleArn': EXECUTION_ROLE,
            'ModelName': 'pca-model',
            'PrimaryContainer': {
                'Environment': {},
                'Image': pca_model.image,
                'ModelDataUrl': pca_model.model_data
            }
        },
        'Resource': 'arn:aws:states:::sagemaker:createModel',
        'End': True
    }
def test_get_expected_model(pca_estimator):
    training_step = TrainingStep('Training', estimator=pca_estimator, job_name='TrainingJob')
    expected_model = training_step.get_expected_model()
    model_step = ModelStep('Create model', model=expected_model, model_name='pca-model')
    assert model_step.to_dict() == {
        'Type': 'Task',
        'Parameters': {
            'ExecutionRoleArn': EXECUTION_ROLE,
            'ModelName': 'pca-model',
            'PrimaryContainer': {
                'Environment': {},
                'Image': expected_model.image,
                'ModelDataUrl.$': "$['ModelArtifacts']['S3ModelArtifacts']"
            }
        },
        'Resource': 'arn:aws:states:::sagemaker:createModel',
        'End': True
    }