def test_model_image(sagemaker_session):
kmeans = KMeans(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet('s3://{}/{}'.format(BUCKET_NAME, PREFIX), num_records=1, feature_dim=FEATURE_DIM, channel='train')
kmeans.fit(data, MINI_BATCH_SIZE)
model = kmeans.create_model()
assert model.image == registry(REGION, 'kmeans') + '/kmeans:1'
def test_call_fit_wrong_value_mini_batch_size(sagemaker_session):
kmeans = KMeans(base_job_name='kmeans', sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet('s3://{}/{}'.format(BUCKET_NAME, PREFIX), num_records=1, feature_dim=FEATURE_DIM,
channel='train')
with pytest.raises(ValueError):
kmeans.fit(data, 0)
def test_model_image(sagemaker_session):
kmeans = KMeans(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet('s3://{}/{}'.format(BUCKET_NAME, PREFIX), num_records=1, feature_dim=FEATURE_DIM, channel='train')
kmeans.fit(data, MINI_BATCH_SIZE)
model = kmeans.create_model()
assert model.image == registry(REGION, 'kmeans') + '/kmeans:1'
def test_predictor_type(sagemaker_session):
kmeans = KMeans(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet('s3://{}/{}'.format(BUCKET_NAME, PREFIX), num_records=1, feature_dim=FEATURE_DIM, channel='train')
kmeans.fit(data, MINI_BATCH_SIZE)
model = kmeans.create_model()
predictor = model.deploy(1, TRAIN_INSTANCE_TYPE)
assert isinstance(predictor, KMeansPredictor)
def test_predictor_type(sagemaker_session):
kmeans = KMeans(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet('s3://{}/{}'.format(BUCKET_NAME, PREFIX), num_records=1, feature_dim=FEATURE_DIM, channel='train')
kmeans.fit(data, MINI_BATCH_SIZE)
model = kmeans.create_model()
predictor = model.deploy(1, TRAIN_INSTANCE_TYPE)
assert isinstance(predictor, KMeansPredictor)
def test_call_fit(base_fit, sagemaker_session):
kmeans = KMeans(base_job_name='kmeans', sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet('s3://{}/{}'.format(BUCKET_NAME, PREFIX), num_records=1, feature_dim=FEATURE_DIM, channel='train')
kmeans.fit(data, MINI_BATCH_SIZE)
base_fit.assert_called_once()
assert len(base_fit.call_args[0]) == 2
assert base_fit.call_args[0][0] == data
assert base_fit.call_args[0][1] == MINI_BATCH_SIZE
def test_call_fit(base_fit, sagemaker_session):
kmeans = KMeans(base_job_name='kmeans', sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet('s3://{}/{}'.format(BUCKET_NAME, PREFIX), num_records=1, feature_dim=FEATURE_DIM, channel='train')
kmeans.fit(data, MINI_BATCH_SIZE)
base_fit.assert_called_once()
assert len(base_fit.call_args[0]) == 2
assert base_fit.call_args[0][0] == data
assert base_fit.call_args[0][1] == MINI_BATCH_SIZE
def test_model_image(sagemaker_session):
kmeans = KMeans(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet(
"s3://{}/{}".format(BUCKET_NAME, PREFIX),
num_records=1,
feature_dim=FEATURE_DIM,
channel="train",
)
kmeans.fit(data, MINI_BATCH_SIZE)
model = kmeans.create_model()
assert image_uris.retrieve("kmeans", REGION) == model.image_uri
def test_predictor_custom_serialization(sagemaker_session):
kmeans = KMeans(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet(
"s3://{}/{}".format(BUCKET_NAME, PREFIX),
num_records=1,
feature_dim=FEATURE_DIM,
channel="train",
)
kmeans.fit(data, MINI_BATCH_SIZE)
model = kmeans.create_model()
custom_serializer = Mock()
custom_deserializer = Mock()
predictor = model.deploy(
1,
INSTANCE_TYPE,
serializer=custom_serializer,
deserializer=custom_deserializer,
)
assert isinstance(predictor, KMeansPredictor)
assert predictor.serializer is custom_serializer
assert predictor.deserializer is custom_deserializer
