fix: use 'sagemaker' logger instead of root logger (#1745)

Author: laurenyu

src/sagemaker/amazon/amazon_estimator.py

@@ -665,7 +665,7 @@ def get_image_uri(region_name, repo_name, repo_version=1):
 
 def _warn_newer_xgboost_image():
     """Print a warning when there is a newer XGBoost image"""
-    logging.warning(
+    logger.warning(
         "There is a more up to date SageMaker XGBoost image. "
         "To use the newer image, please set 'repo_version'="
         "'%s'. For example:\n"

src/sagemaker/analytics.py

@@ -23,11 +23,12 @@
 from sagemaker.session import Session
 from sagemaker.utils import DeferredError
 
+logger = logging.getLogger(__name__)
 
 try:
     import pandas as pd
 except ImportError as e:
-    logging.warning("pandas failed to import. Analytics features will be impaired or broken.")
+    logger.warning("pandas failed to import. Analytics features will be impaired or broken.")
     # Any subsequent attempt to use pandas will raise the ImportError
     pd = DeferredError(e)
 
@@ -251,15 +252,13 @@ def training_job_summaries(self, force_refresh=False):
         output = []
         next_args = {}
         for count in range(100):
-            logging.debug("Calling list_training_jobs_for_hyper_parameter_tuning_job %d", count)
+            logger.debug("Calling list_training_jobs_for_hyper_parameter_tuning_job %d", count)
             raw_result = self._sage_client.list_training_jobs_for_hyper_parameter_tuning_job(
                 HyperParameterTuningJobName=self.name, MaxResults=100, **next_args
             )
             new_output = raw_result["TrainingJobSummaries"]
             output.extend(new_output)
-            logging.debug(
-                "Got %d more TrainingJobs. Total so far: %d", len(new_output), len(output)
-            )
+            logger.debug("Got %d more TrainingJobs. Total so far: %d", len(new_output), len(output))
             if ("NextToken" in raw_result) and (len(new_output) > 0):
                 next_args["NextToken"] = raw_result["NextToken"]
             else:
@@ -373,7 +372,7 @@ def _fetch_metric(self, metric_name):
         }
         raw_cwm_data = self._cloudwatch.get_metric_statistics(**request)["Datapoints"]
         if len(raw_cwm_data) == 0:
-            logging.warning("Warning: No metrics called %s found", metric_name)
+            logger.warning("Warning: No metrics called %s found", metric_name)
             return
 
         # Process data: normalize to starting time, and sort.

src/sagemaker/estimator.py

@@ -59,6 +59,8 @@
 from sagemaker.utils import base_name_from_image, name_from_base, get_config_value
 from sagemaker import vpc_utils
 
+logger = logging.getLogger(__name__)
+
 
 class EstimatorBase(with_metaclass(ABCMeta, object)):
     """Handle end-to-end Amazon SageMaker training and deployment tasks.
@@ -730,7 +732,7 @@ def model_data(self):
                 TrainingJobName=self.latest_training_job.name
             )["ModelArtifacts"]["S3ModelArtifacts"]
         else:
-            logging.warning(
+            logger.warning(
                 "No finished training job found associated with this estimator. Please make sure "
                 "this estimator is only used for building workflow config"
             )
@@ -826,7 +828,7 @@ def delete_endpoint(self):
         Raises:
             botocore.exceptions.ClientError: If the endpoint does not exist.
         """
-        logging.warning(
+        logger.warning(
             "estimator.delete_endpoint() will be deprecated in SageMaker Python SDK v2. "
             "Please use the delete_endpoint() function on your predictor instead."
         )
@@ -908,7 +910,7 @@ def transformer(
         tags = tags or self.tags
 
         if self.latest_training_job is None:
-            logging.warning(
+            logger.warning(
                 "No finished training job found associated with this estimator. Please make sure "
                 "this estimator is only used for building workflow config"
             )
@@ -1029,7 +1031,7 @@ def start_new(cls, estimator, inputs, experiment_config):
 
         if isinstance(inputs, s3_input):
             if "InputMode" in inputs.config:
-                logging.debug(
+                logger.debug(
                     "Selecting s3_input's input_mode (%s) for TrainingInputMode.",
                     inputs.config["InputMode"],
                 )
@@ -1279,7 +1281,7 @@ def __init__(
                 https://docs.aws.amazon.com/sagemaker/latest/dg/API_AlgorithmSpecification.html#SageMaker-Type-AlgorithmSpecification-EnableSageMakerMetricsTimeSeries
                 (default: ``None``).
         """
-        logging.warning(parameter_v2_rename_warning("image_name", "image_uri"))
+        logger.warning(parameter_v2_rename_warning("image_name", "image_uri"))
         self.image_name = image_name
         self.hyperparam_dict = hyperparameters.copy() if hyperparameters else {}
         super(Estimator, self).__init__(
@@ -1653,7 +1655,7 @@ def __init__(
         self.code_location = code_location
         self.image_name = image_name
         if image_name is not None:
-            logging.warning(parameter_v2_rename_warning("image_name", "image_uri"))
+            logger.warning(parameter_v2_rename_warning("image_name", "image_uri"))
 
         self.uploaded_code = None
 
@@ -2029,7 +2031,7 @@ def transformer(
             if env is not None:
                 transform_env.update(env)
         else:
-            logging.warning(
+            logger.warning(
                 "No finished training job found associated with this estimator. Please make sure "
                 "this estimator is only used for building workflow config"
             )

src/sagemaker/fw_registry.py

@@ -12,10 +12,13 @@
 # language governing permissions and limitations under the License.
 """Placeholder docstring"""
 from __future__ import absolute_import
+
 import logging
 
 from sagemaker.utils import get_ecr_image_uri_prefix
 
+logger = logging.getLogger(__name__)
+
 image_registry_map = {
     "us-west-1": {
         "sparkml-serving": "746614075791",
@@ -142,7 +145,7 @@ def registry(region_name, framework=None):
         account_id = image_registry_map[region_name][framework]
         return get_ecr_image_uri_prefix(account_id, region_name)
     except KeyError:
-        logging.error("The specific image or region does not exist")
+        logger.error("The specific image or region does not exist")
         raise
 
 

src/sagemaker/local/entities.py

@@ -25,16 +25,15 @@
 from sagemaker.local.utils import copy_directory_structure, move_to_destination
 from sagemaker.utils import DeferredError, get_config_value
 
+logger = logging.getLogger(__name__)
+
 try:
     import urllib3
 except ImportError as e:
-    logging.warning("urllib3 failed to import. Local mode features will be impaired or broken.")
+    logger.warning("urllib3 failed to import. Local mode features will be impaired or broken.")
     # Any subsequent attempt to use urllib3 will raise the ImportError
     urllib3 = DeferredError(e)
 
-
-logger = logging.getLogger(__name__)
-
 _UNUSED_ARN = "local:arn-does-not-matter"
 HEALTH_CHECK_TIMEOUT_LIMIT = 120
 

src/sagemaker/local/image.py

@@ -473,7 +473,7 @@ def _generate_compose_file(self, command, additional_volumes=None, additional_en
         try:
             import yaml
         except ImportError as e:
-            logging.error(sagemaker.utils._module_import_error("yaml", "Local mode", "local"))
+            logger.error(sagemaker.utils._module_import_error("yaml", "Local mode", "local"))
             raise e
 
         yaml_content = yaml.dump(content, default_flow_style=False)

src/sagemaker/local/local_session.py

@@ -327,7 +327,7 @@ def __init__(self, config=None):
         try:
             import urllib3
         except ImportError as e:
-            logging.error(_module_import_error("urllib3", "Local mode", "local"))
+            logger.error(_module_import_error("urllib3", "Local mode", "local"))
             raise e
 
         self.http = urllib3.PoolManager()
@@ -423,7 +423,7 @@ def _initialize(self, boto_session, sagemaker_client, sagemaker_runtime_client):
             try:
                 import yaml
             except ImportError as e:
-                logging.error(_module_import_error("yaml", "Local mode", "local"))
+                logger.error(_module_import_error("yaml", "Local mode", "local"))
                 raise e
 
             self.config = yaml.load(open(sagemaker_config_file, "r"))

src/sagemaker/tensorflow/estimator.py

@@ -856,7 +856,7 @@ def transformer(
         role = role or self.role
 
         if self.latest_training_job is None:
-            logging.warning(
+            logger.warning(
                 "No finished training job found associated with this estimator. Please make sure "
                 "this estimator is only used for building workflow config"
             )

src/sagemaker/tensorflow/serving.py

@@ -21,6 +21,8 @@
 from sagemaker.predictor import json_serializer, json_deserializer
 from sagemaker.tensorflow.defaults import TF_VERSION
 
+logger = logging.getLogger(__name__)
+
 
 class Predictor(sagemaker.RealTimePredictor):
     """A ``RealTimePredictor`` implementation for inference against TensorFlow
@@ -255,7 +257,7 @@ def _get_container_env(self):
             return self.env
 
         if self._container_log_level not in Model.LOG_LEVEL_MAP:
-            logging.warning("ignoring invalid container log level: %s", self._container_log_level)
+            logger.warning("ignoring invalid container log level: %s", self._container_log_level)
             return self.env
 
         env = dict(self.env)

src/sagemaker/tuner.py

@@ -54,6 +54,8 @@
 PARENT_HYPERPARAMETER_TUNING_JOBS = "ParentHyperParameterTuningJobs"
 WARM_START_TYPE = "WarmStartType"
 
+logger = logging.getLogger(__name__)
+
 
 class WarmStartTypes(Enum):
     """Warm Start Configuration type. There can be two types of warm start jobs:
@@ -840,7 +842,7 @@ def delete_endpoint(self, endpoint_name=None):
         Args:
             endpoint_name (str): Name of the endpoint to delete
         """
-        logging.warning(
+        logger.warning(
             "HyperparameterTuner.delete_endpoint() will be deprecated in SageMaker Python SDK v2. "
             "Please use the delete_endpoint() function on your predictor instead."
         )
@@ -1459,7 +1461,7 @@ def start_new(cls, tuner, inputs):
             information about the started job.
         """
 
-        logging.info("_TuningJob.start_new!!!")
+        logger.info("_TuningJob.start_new!!!")
 
         warm_start_config_req = None
         if tuner.warm_start_config:
@@ -1529,7 +1531,7 @@ def _prepare_training_config(
 
         if isinstance(inputs, s3_input):
             if "InputMode" in inputs.config:
-                logging.debug(
+                logger.debug(
                     "Selecting s3_input's input_mode (%s) for TrainingInputMode.",
                     inputs.config["InputMode"],
                 )