process -> thread

ClickThroughRate/WideDeepLearning/wdl_train_eval.py

@@ -27,11 +27,11 @@ def str_list(x):
 parser = argparse.ArgumentParser()
 parser.add_argument('--dataset_format', type=str, default='ofrecord', help='ofrecord or onerec')
 parser.add_argument(
-    "--use_single_dataloader_process",
+    "--use_single_dataloader_thread",
     action="store_true",
-    help="use single dataloader processes per node or not."
+    help="use single dataloader threads per node or not."
 )
-parser.add_argument('--num_dataloader_process_per_gpu', type=int, default=1)
+parser.add_argument('--num_dataloader_thread_per_gpu', type=int, default=2)
 parser.add_argument('--train_data_dir', type=str, default='')
 parser.add_argument('--train_data_part_num', type=int, default=1)
 parser.add_argument('--train_part_name_suffix_length', type=int, default=-1)
@@ -66,12 +66,12 @@ def str_list(x):
 DEEP_HIDDEN_UNITS = [FLAGS.hidden_size for i in range(FLAGS.hidden_units_num)]
 
 def _data_loader(data_dir, data_part_num, batch_size, part_name_suffix_length=-1, shuffle=True):
-    assert FLAGS.num_dataloader_process_per_gpu >= 1
-    if FLAGS.use_single_dataloader_process:
+    assert FLAGS.num_dataloader_thread_per_gpu >= 1
+    if FLAGS.use_single_dataloader_thread:
         devices = ['{}:0'.format(i) for i in range(FLAGS.num_nodes)]
     else:
-        num_dataloader_process = FLAGS.num_dataloader_process_per_gpu * FLAGS.gpu_num_per_node
-        devices = ['{}:0-{}'.format(i, num_dataloader_process - 1) for i in range(FLAGS.num_nodes)]
+        num_dataloader_thread = FLAGS.num_dataloader_thread_per_gpu * FLAGS.gpu_num_per_node
+        devices = ['{}:0-{}'.format(i, num_dataloader_thread - 1) for i in range(FLAGS.num_nodes)]
     with flow.scope.placement("cpu", devices):
         if FLAGS.dataset_format == 'ofrecord':
             data = _data_loader_ofrecord(data_dir, data_part_num, batch_size,