diff --git a/dlio_benchmark/data_loader/tf_data_loader.py b/dlio_benchmark/data_loader/tf_data_loader.py
index 162e6c1f..695c4523 100644
--- a/dlio_benchmark/data_loader/tf_data_loader.py
+++ b/dlio_benchmark/data_loader/tf_data_loader.py
@@ -95,7 +95,7 @@ def read(self):
             self._dataset = ReaderFactory.get_reader(type=self.format_type,
                                           dataset_type=self.dataset_type,
                                           thread_index=-1,
-                                          epoch_number=0).next()
+                                          epoch_number=self.epoch_number).next()
 
     @dlp.log
     def next(self):
diff --git a/dlio_benchmark/reader/tf_reader.py b/dlio_benchmark/reader/tf_reader.py
index ce37b925..dc496039 100644
--- a/dlio_benchmark/reader/tf_reader.py
+++ b/dlio_benchmark/reader/tf_reader.py
@@ -81,12 +81,13 @@ def _parse_image(self, serialized):
 
     @dlp.log
     def next(self):
-        logging.debug(f"{utcnow()} Reading {len(self._file_list)} files thread {self.thread_index} rank {self._args.my_rank}")
-        filenames = tf.data.Dataset.list_files(self._file_list, shuffle=True)
+        logging.debug(f"{utcnow()} Reading {self._file_list} files thread {self.thread_index} rank {self._args.my_rank}")
+        filenames = tf.data.Dataset.list_files(self._file_list, shuffle=False)
         # sharding in the file list if we have enought files. 
         if (len(self._file_list) >= self._args.comm_size):
             filenames = filenames.shard(num_shards=self._args.comm_size, index=self._args.my_rank)
-			
+            logging.debug(f"{utcnow()} shard {filenames} files index {self._args.my_rank} number {self._args.comm_size}")
+        
         self._dataset = tf.data.TFRecordDataset(filenames=filenames, buffer_size=self._args.transfer_size,
                                                 num_parallel_reads=self._args.read_threads)
 				  
diff --git a/dlio_benchmark/utils/config.py b/dlio_benchmark/utils/config.py
index 8e25b148..a0264553 100644
--- a/dlio_benchmark/utils/config.py
+++ b/dlio_benchmark/utils/config.py
@@ -138,6 +138,7 @@ class ConfigArguments:
     data_loader_class = None
     reader_class = None
     checkpoint_mechanism_class = None
+    native_data_loader = False
 
     def __init__(self):
         """ Virtually private constructor. """
@@ -300,6 +301,13 @@ def derive_configurations(self, file_list_train=None, file_list_eval=None):
                         logging.info(f"Discovered custom data reader {class_name}")
                     self.reader_class = obj
                     break
+        self.native_data_loader = False
+        if self.data_loader == DataLoaderType.TENSORFLOW:
+            if self.format == FormatType.TFRECORD:
+                self.native_data_loader = True
+        elif self.data_loader == DataLoaderType.NATIVE_DALI:
+            if self.format in [FormatType.JPEG, FormatType.PNG, FormatType.NPY, FormatType.TFRECORD]:
+                self.native_data_loader = True
 
     @dlp.log
     def build_sample_map_iter(self, file_list, total_samples, epoch_number):
@@ -363,24 +371,27 @@ def get_global_map_index(self, file_list, total_samples):
     def reconfigure(self, epoch_number, dataset_type):
         if self.data_loader_sampler == DataLoaderSampler.ITERATIVE:
             if self.file_shuffle is not Shuffle.OFF:
-                if self.seed_change_epoch:
-                    np.random.seed(self.seed + epoch_number)
-                else:
-                    np.random.seed(self.seed)
+                if self.file_shuffle is Shuffle.SEED:
+                    if self.seed_change_epoch:
+                        np.random.seed(self.seed + epoch_number)
+                    else:
+                        np.random.seed(self.seed)
                 np.random.shuffle(self.file_list_train) if dataset_type is DatasetType.TRAIN else np.random.shuffle(
                     self.file_list_eval)
-        if self.data_loader_sampler == DataLoaderSampler.ITERATIVE:
-            if dataset_type is DatasetType.TRAIN:
-                global_file_map = self.build_sample_map_iter(self.file_list_train, self.total_samples_train,
-                                                             epoch_number)
-            else:
-                global_file_map = self.build_sample_map_iter(self.file_list_eval, self.total_samples_eval, epoch_number)
-            self.file_map = global_file_map[self.my_rank]
-        elif self.data_loader_sampler == DataLoaderSampler.INDEX:
-            if dataset_type is DatasetType.TRAIN:
-                self.global_index_map = self.get_global_map_index(self.file_list_train, self.total_samples_train)
-            else:
-                self.global_index_map = self.get_global_map_index(self.file_list_eval, self.total_samples_eval)
+        # the code assumes that file and sample shuffling is handled by the native data loader code.
+        if not self.native_data_loader:
+            if self.data_loader_sampler == DataLoaderSampler.ITERATIVE:
+                if dataset_type is DatasetType.TRAIN:
+                    global_file_map = self.build_sample_map_iter(self.file_list_train, self.total_samples_train,
+                                                                epoch_number)
+                else:
+                    global_file_map = self.build_sample_map_iter(self.file_list_eval, self.total_samples_eval, epoch_number)
+                self.file_map = global_file_map[self.my_rank]
+            elif self.data_loader_sampler == DataLoaderSampler.INDEX:
+                if dataset_type is DatasetType.TRAIN:
+                    self.global_index_map = self.get_global_map_index(self.file_list_train, self.total_samples_train)
+                else:
+                    self.global_index_map = self.get_global_map_index(self.file_list_eval, self.total_samples_eval)
 
 
 def LoadConfig(args, config):