diff --git a/kraken/lib/layers.py b/kraken/lib/layers.py
index a937fdd32..984c59996 100644
--- a/kraken/lib/layers.py
+++ b/kraken/lib/layers.py
@@ -10,11 +10,11 @@
 from torch.nn import functional as F
 from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
 
-logger = logging.getLogger('coremltools')
-logger.setLevel(logging.ERROR)
+root_logger = logging.getLogger()
+level = root_logger.getEffectiveLevel()
+root_logger.setLevel(logging.ERROR)
 from coremltools.proto import NeuralNetwork_pb2  # NOQA
-
-logger.setLevel(logging.WARNING)
+root_logger.setLevel(level)
 
 # all tensors are ordered NCHW, the "feature" dimension is C, so the output of
 # an LSTM will be put into C same as the filters of a CNN.
diff --git a/kraken/lib/vgsl.py b/kraken/lib/vgsl.py
index 9a4f0759b..083abbe25 100644
--- a/kraken/lib/vgsl.py
+++ b/kraken/lib/vgsl.py
@@ -9,8 +9,6 @@
                     Tuple, Union)
 
 import torch
-from coremltools.models import MLModel, datatypes
-from coremltools.models.neural_network import NeuralNetworkBuilder
 from google.protobuf.message import DecodeError
 from torch import nn
 
@@ -18,6 +16,13 @@
 from kraken.lib.codec import PytorchCodec
 from kraken.lib.exceptions import KrakenInvalidModelException
 
+root_logger = logging.getLogger()
+level = root_logger.getEffectiveLevel()
+root_logger.setLevel(logging.ERROR)
+from coremltools.models import MLModel, datatypes
+from coremltools.models.neural_network import NeuralNetworkBuilder
+root_logger.setLevel(level)
+
 # all tensors are ordered NCHW, the "feature" dimension is C, so the output of
 # an LSTM will be put into C same as the filters of a CNN.