fix: remove logging level overrides

Author: laurenyu

src/sagemaker/chainer/estimator.py

@@ -20,7 +20,6 @@
 from sagemaker.chainer.model import ChainerModel
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
 
-logging.basicConfig()
 logger = logging.getLogger('sagemaker')
 
 

src/sagemaker/local/entities.py

@@ -26,7 +26,6 @@
 from sagemaker.utils import get_config_value
 
 logger = logging.getLogger(__name__)
-logger.setLevel(logging.WARNING)
 
 _UNUSED_ARN = 'local:arn-does-not-matter'
 HEALTH_CHECK_TIMEOUT_LIMIT = 120

src/sagemaker/local/image.py

@@ -49,7 +49,6 @@
 TRAINING_JOB_NAME_ENV_NAME = 'TRAINING_JOB_NAME'
 
 logger = logging.getLogger(__name__)
-logger.setLevel(logging.WARNING)
 
 
 class _SageMakerContainer(object):

src/sagemaker/local/local_session.py

@@ -26,7 +26,6 @@
 from sagemaker.utils import get_config_value
 
 logger = logging.getLogger(__name__)
-logger.setLevel(logging.WARNING)
 
 
 class LocalSagemakerClient(object):

src/sagemaker/model.py

@@ -19,9 +19,7 @@
 from sagemaker import fw_utils, local, session, utils
 from sagemaker.transformer import Transformer
 
-logging.basicConfig()
 LOGGER = logging.getLogger('sagemaker')
-LOGGER.setLevel(logging.INFO)
 
 NEO_ALLOWED_TARGET_INSTANCE_FAMILY = set(['ml_c5', 'ml_m5', 'ml_c4', 'ml_m4', 'jetson_tx1', 'jetson_tx2', 'ml_p2',
                                           'ml_p3', 'deeplens', 'rasp3b'])

src/sagemaker/mxnet/estimator.py

@@ -20,7 +20,6 @@
 from sagemaker.mxnet.model import MXNetModel
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
 
-logging.basicConfig()
 logger = logging.getLogger('sagemaker')
 
 

src/sagemaker/pytorch/estimator.py

@@ -20,7 +20,6 @@
 from sagemaker.pytorch.model import PyTorchModel
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
 
-logging.basicConfig()
 logger = logging.getLogger('sagemaker')
 
 

src/sagemaker/rl/estimator.py

@@ -22,7 +22,6 @@
 from sagemaker.mxnet.model import MXNetModel
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
 
-logging.basicConfig()
 logger = logging.getLogger('sagemaker')
 
 SAGEMAKER_ESTIMATOR = 'sagemaker_estimator'

src/sagemaker/session.py

@@ -31,10 +31,7 @@
 from sagemaker.user_agent import prepend_user_agent
 from sagemaker.utils import name_from_image, secondary_training_status_changed, secondary_training_status_message
 
-logging.basicConfig()
 LOGGER = logging.getLogger('sagemaker')
-LOGGER.setLevel(logging.INFO)
-
 
 _STATUS_CODE_TABLE = {
     'COMPLETED': 'Completed',

src/sagemaker/sklearn/estimator.py

@@ -21,7 +21,6 @@
 from sagemaker.sklearn.model import SKLearnModel
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
 
-logging.basicConfig()
 logger = logging.getLogger('sagemaker')
 
 

src/sagemaker/tensorflow/estimator.py

@@ -29,7 +29,6 @@
 from sagemaker.utils import get_config_value
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
 
-logging.basicConfig()
 LOGGER = logging.getLogger('sagemaker')