multi node training

examples/test.py

@@ -1,3 +1,10 @@
+import sys
+import os
+
+sys.path.append(
+    os.path.abspath(os.path.join(os.path.dirname(__file__), '../'))
+)
+
 import torch
 import os
 import glob
@@ -62,7 +69,7 @@ def load_remote():
 hifigan = load_local().to(device)
 
 # Load audio
-wav, sr = torchaudio.load("zszy_48k.wav")
+wav, sr = torchaudio.load("7200000318_0_generated.wav")
 assert sr == 48000
 
 # mel = mel_spectrogram_torch(wav, 2048, 128, 48000, 512, 2048, 0, None, False)

train.py

@@ -46,6 +46,7 @@ def main():
     parser.add_argument('-c', '--config', type=str, default="./configs/48k.json", help='JSON file for configuration')
     parser.add_argument('-a', '--accelerator', type=str, default="gpu", help='training device')
     parser.add_argument('-d', '--device', type=str, default="0", help='training device ids')
+    parser.add_argument('-n', '--num-nodes', type=int, default=1, help='training node number')
     args = parser.parse_args()
 
     hparams = get_hparams(args.config)
@@ -75,6 +76,8 @@ def main():
     if hparams.train.fp16_run:
         trainer_params["amp_backend"] = "native"
         trainer_params["precision"] = 16
+
+    trainer_params["num_nodes"] = args.num_nodes
 
     # data
     train_dataset = MelDataset(hparams.data.training_files, hparams.data)