parallel

nnet/xvector.py

@@ -1067,7 +1067,7 @@ def get_loaders(dataset_opts, training_opts, model_opts, speaker_number):
     Set the dataloaders according to the dataset_yaml
     
     First we load the dataframe from CSV file in order to split it for training and validation purpose
-    Then we provide those two 
+    Then we provide those two
     """
     df = pandas.read_csv(dataset_opts["dataset_csv"])
 
@@ -1294,7 +1294,7 @@ def new_xtrain(dataset_description,
     torch.manual_seed(training_opts["seed"])
     torch.cuda.manual_seed(training_opts["seed"])
 
-    # Display the entire configurations as YAML dictionnaries
+    # Display the entire configurations as YAML dictionaries
     if local_rank < 1:
         monitor.logger.info("\n*********************************\nDataset options\n*********************************\n")
         monitor.logger.info(yaml.dump(dataset_opts, default_flow_style=False))
@@ -1309,11 +1309,12 @@ def new_xtrain(dataset_description,
     embedding_size = model.embedding_size
 
     # Set the device and manage parallel processing
-    device = torch.cuda.device(local_rank)
+    #device = torch.cuda.device(local_rank)
     torch.cuda.set_device(local_rank)
+    device = torch.device(local_rank)
     model.to(device)
 
-    # If multi-gpu
+
     """ [HOW TO] from https://gist.github.com/sgraaf/5b0caa3a320f28c27c12b5efeb35aa4c
         - Add the following line right after "if __name__ == '__main__':" in your main script :
         parser.add_argument('--local_rank', type=int, default=-1, metavar='N', help='Local process rank.')