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