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