diff --git a/tuning/data/data_config.py b/tuning/data/data_config.py
index b95b996fe..0c5521baf 100644
--- a/tuning/data/data_config.py
+++ b/tuning/data/data_config.py
@@ -21,6 +21,8 @@
 # Local
 from tuning.utils.utils import load_yaml_or_json
 
+logger = logging.getLogger(__name__)
+
 
 @dataclass
 class DataHandlerConfig:
@@ -82,9 +84,7 @@ def _validate_dataset_config(dataset_config) -> DataSetConfig:
         assert isinstance(p, str), f"path {p} should be of the type string"
         if not os.path.isabs(p):
             _p = os.path.abspath(p)
-            logging.warning(
-                " Provided path %s is not absolute changing it to %s", p, _p
-            )
+            logger.warning(" Provided path %s is not absolute changing it to %s", p, _p)
             p = _p
         c.data_paths.append(p)
     if "builder" in kwargs and kwargs["builder"] is not None:
diff --git a/tuning/data/data_processors.py b/tuning/data/data_processors.py
index dbce7aedc..170bc2a81 100644
--- a/tuning/data/data_processors.py
+++ b/tuning/data/data_processors.py
@@ -29,6 +29,8 @@
 from tuning.data.data_handlers import AVAILABLE_DATA_HANDLERS
 from tuning.utils.utils import get_loader_for_filepath, validate_mergeable_datasets
 
+logger = logging.getLogger(__name__)
+
 
 class DataPreProcessor:
 
@@ -54,11 +56,11 @@ def register_data_handler(self, name: str, func: Callable):
         if not isinstance(name, str) or not callable(func):
             raise ValueError("Handlers should be of type Dict, str to callable")
         if name in self.registered_handlers:
-            logging.warning(
+            logger.warning(
                 "Handler name '%s' already exists and will be overwritten", name
             )
         self.registered_handlers[name] = func
-        logging.info("Registered new handler %s", name)
+        logger.info("Registered new handler %s", name)
 
     def register_data_handlers(self, handlers: Dict[str, Callable]):
         if handlers is None:
@@ -175,7 +177,7 @@ def _load_dataset(data_path=None, builder=None, data_files=None, data_dir=None):
                 return all_datasets[0]
 
             raw_datasets = datasets.concatenate_datasets(all_datasets)
-            logging.info(
+            logger.info(
                 "Datasets concatenated from %s .Concatenated dataset columns: %s",
                 datasetconfig.name,
                 list(raw_datasets.features.keys()),
@@ -207,25 +209,25 @@ def _process_dataset_configs(
             if sum(p for p in sampling_probabilities) != 1:
                 raise ValueError("Sampling probabilities don't sum to 1")
             sample_datasets = True
-            logging.info(
+            logger.info(
                 "Sampling ratios are specified; given datasets will be interleaved."
             )
         else:
-            logging.info(
+            logger.info(
                 "Sampling is not specified; if multiple datasets are provided,"
                 " the given datasets will be concatenated."
             )
             sample_datasets = False
 
-        logging.info("Starting DataPreProcessor...")
+        logger.info("Starting DataPreProcessor...")
         # Now Iterate over the multiple datasets provided to us to process
         for d in dataset_configs:
-            logging.info("Loading %s", d.name)
+            logger.info("Loading %s", d.name)
 
             # In future the streaming etc go as kwargs of this function
             raw_dataset = self.load_dataset(d, splitName)
 
-            logging.info("Loaded raw dataset : %s", str(raw_dataset))
+            logger.info("Loaded raw dataset : %s", str(raw_dataset))
 
             raw_datasets = DatasetDict()
 
@@ -266,7 +268,7 @@ def _process_dataset_configs(
 
                     kwargs["fn_kwargs"] = dict(kwargs["fn_kwargs"], **extra_kwargs)
 
-                    logging.info("Applying Handler: %s Args: %s", data_handler, kwargs)
+                    logger.info("Applying Handler: %s Args: %s", data_handler, kwargs)
 
                     raw_datasets = raw_datasets.map(handler, **kwargs)
 
@@ -285,7 +287,7 @@ def _process_dataset_configs(
         if sample_datasets:
             strategy = self.processor_config.sampling_stopping_strategy
             seed = self.processor_config.sampling_seed
-            logging.info(
+            logger.info(
                 "Interleaving datasets: strategy[%s] seed[%d] probabilities[%s]",
                 strategy,
                 seed,
@@ -316,7 +318,7 @@ def process_dataset_configs(
 
         if torch.distributed.is_available() and torch.distributed.is_initialized():
             if torch.distributed.get_rank() == 0:
-                logging.info("Processing data on rank 0...")
+                logger.info("Processing data on rank 0...")
                 train_dataset = self._process_dataset_configs(dataset_configs, **kwargs)
             else:
                 train_dataset = None
@@ -329,7 +331,7 @@ def process_dataset_configs(
             torch.distributed.broadcast_object_list(to_share, src=0)
             train_dataset = to_share[0]
         else:
-            logging.info("Processing data...")
+            logger.info("Processing data...")
             train_dataset = self._process_dataset_configs(dataset_configs, **kwargs)
 
         return train_dataset
diff --git a/tuning/data/setup_dataprocessor.py b/tuning/data/setup_dataprocessor.py
index 2bae18bb7..b6f09c323 100644
--- a/tuning/data/setup_dataprocessor.py
+++ b/tuning/data/setup_dataprocessor.py
@@ -33,6 +33,8 @@
 from tuning.data.data_preprocessing_utils import get_data_collator
 from tuning.data.data_processors import get_datapreprocessor
 
+logger = logging.getLogger(__name__)
+
 # In future we may make the fields configurable
 DEFAULT_INPUT_COLUMN = "input"
 DEFAULT_OUTPUT_COLUMN = "output"
@@ -320,9 +322,9 @@ def process_dataargs(
     """
 
     max_seq_length = min(train_args.max_seq_length, tokenizer.model_max_length)
-    logging.info("Max sequence length is %s", max_seq_length)
+    logger.info("Max sequence length is %s", max_seq_length)
     if train_args.max_seq_length > tokenizer.model_max_length:
-        logging.warning(
+        logger.warning(
             "max_seq_length %s exceeds tokenizer.model_max_length \
             %s, using tokenizer.model_max_length %s",
             train_args.max_seq_length,