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
}