def test_estimator_transformer_creation_with_optional_params(sagemaker_session):
base_name = 'foo'
estimator = Estimator(image_name=IMAGE_NAME, role=ROLE, train_instance_count=INSTANCE_COUNT,
train_instance_type=INSTANCE_TYPE, sagemaker_session=sagemaker_session,
base_job_name=base_name)
estimator.latest_training_job = _TrainingJob(sagemaker_session, JOB_NAME)
sagemaker_session.create_model_from_job.return_value = JOB_NAME
strategy = 'MultiRecord'
assemble_with = 'Line'
kms_key = 'key'
accept = 'text/csv'
max_concurrent_transforms = 1
max_payload = 6
env = {'FOO': 'BAR'}
transformer = estimator.transformer(INSTANCE_COUNT, INSTANCE_TYPE, strategy=strategy, assemble_with=assemble_with,
output_path=OUTPUT_PATH, output_kms_key=kms_key, accept=accept, tags=TAGS,
max_concurrent_transforms=max_concurrent_transforms, max_payload=max_payload,
env=env, role=ROLE)
sagemaker_session.create_model_from_job.assert_called_with(JOB_NAME, role=ROLE)
assert transformer.strategy == strategy
assert transformer.assemble_with == assemble_with
assert transformer.output_path == OUTPUT_PATH
assert transformer.output_kms_key == kms_key
assert transformer.accept == accept
assert transformer.max_concurrent_transforms == max_concurrent_transforms
assert transformer.max_payload == max_payload
assert transformer.env == env
assert transformer.base_transform_job_name == base_name
assert transformer.tags == TAGS
def test_estimator_transformer_creation(sagemaker_session):
estimator = Estimator(image_name=IMAGE_NAME, role=ROLE, train_instance_count=INSTANCE_COUNT,
train_instance_type=INSTANCE_TYPE, sagemaker_session=sagemaker_session)
estimator.latest_training_job = _TrainingJob(sagemaker_session, JOB_NAME)
sagemaker_session.create_model_from_job.return_value = JOB_NAME
transformer = estimator.transformer(INSTANCE_COUNT, INSTANCE_TYPE)
sagemaker_session.create_model_from_job.assert_called_with(JOB_NAME, role=None)
assert isinstance(transformer, Transformer)
assert transformer.sagemaker_session == sagemaker_session
assert transformer.instance_count == INSTANCE_COUNT
assert transformer.instance_type == INSTANCE_TYPE
assert transformer.model_name == JOB_NAME
assert transformer.tags is None
def main():
download_training_and_eval_data()
image = 'sagemaker-tensorflow2-batch-transform-local'
env = {
"MODEL_SERVER_WORKERS": "2"
}
print('Starting model training.')
california_housing_estimator = Estimator(
image,
DUMMY_IAM_ROLE,
hyperparameters={'epochs': 10,
'batch_size': 64,
'learning_rate': 0.1},
instance_count=1,
instance_type="local")
inputs = {'train': 'file://./data/train', 'test': 'file://./data/test'}
california_housing_estimator.fit(inputs, logs=True)
print('Completed model training')
print('Running Batch Transform in local mode')
tensorflow_serving_transformer = california_housing_estimator.transformer(
instance_count=1,
instance_type='local',
output_path='file:./data/output',
env = env
)
tensorflow_serving_transformer.transform('file://./data/input',
split_type='Line',
content_type='text/csv')
print('Printing Batch Transform output file content')
output_file = open('./data/output/x_test.csv.out', 'r').read()
print(output_file)