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