def test_model_image(sagemaker_session):
lr = LinearLearner(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet('s3://{}/{}'.format(BUCKET_NAME, PREFIX), num_records=1, feature_dim=FEATURE_DIM, channel='train')
lr.fit(data)
model = lr.create_model()
assert model.image == registry(REGION, 'linear-learner') + '/linear-learner:1'
def test_predictor_type(sagemaker_session):
lr = LinearLearner(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet('s3://{}/{}'.format(BUCKET_NAME, PREFIX), num_records=1, feature_dim=FEATURE_DIM, channel='train')
lr.fit(data)
model = lr.create_model()
predictor = model.deploy(1, TRAIN_INSTANCE_TYPE)
assert isinstance(predictor, LinearLearnerPredictor)
def test_model_image(sagemaker_session):
lr = LinearLearner(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet(
"s3://{}/{}".format(BUCKET_NAME, PREFIX),
num_records=1,
feature_dim=FEATURE_DIM,
channel="train",
)
lr.fit(data)
model = lr.create_model()
assert image_uris.retrieve("linear-learner", REGION) == model.image_uri
def test_predictor_custom_serialization(sagemaker_session):
lr = LinearLearner(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet(
"s3://{}/{}".format(BUCKET_NAME, PREFIX),
num_records=1,
feature_dim=FEATURE_DIM,
channel="train",
)
lr.fit(data)
model = lr.create_model()
custom_serializer = Mock()
custom_deserializer = Mock()
predictor = model.deploy(
1,
INSTANCE_TYPE,
serializer=custom_serializer,
deserializer=custom_deserializer,
)
assert isinstance(predictor, LinearLearnerPredictor)
assert predictor.serializer is custom_serializer
assert predictor.deserializer is custom_deserializer
