Unusual printing statements after 90% epoch completition
i've encountered this unusual print statements while training
It seems that this printing starts when epoch is 90% complete and both loss and train_loss is same until 100% completition
This behaviour is same on TPU's as well as on GPU's
2020-10-05 11:32:55.426605: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
CUDA_VISIBLE_DEVICES: [0]
/usr/local/lib/python3.6/dist-packages/pytorch_lightning/utilities/distributed.py:37: UserWarning: Could not log computational graph since the `model.example_input_array` attribute is not set or `input_array` was not given
  warnings.warn(*args, **kwargs)

  | Name      | Type      | Params
----------------------------------------
0 | model     | Predictor | 44 M  
1 | criterion | MSELoss   | 0     
Epoch 0:  90% 1531/1702 [12:41<01:25,  2.01it/s, loss=0.158, v_num=0, train_loss=0.149]
Validating: 0it [00:00, ?it/s]
Epoch 0:  90% 1532/1702 [12:41<01:24,  2.01it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  90% 1533/1702 [12:41<01:23,  2.01it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  90% 1534/1702 [12:41<01:23,  2.01it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  90% 1535/1702 [12:41<01:22,  2.01it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  90% 1536/1702 [12:42<01:22,  2.02it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  90% 1537/1702 [12:42<01:21,  2.02it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  90% 1538/1702 [12:42<01:21,  2.02it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  90% 1539/1702 [12:42<01:20,  2.02it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  90% 1540/1702 [12:42<01:20,  2.02it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  91% 1541/1702 [12:42<01:19,  2.02it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  91% 1542/1702 [12:43<01:19,  2.02it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  91% 1543/1702 [12:43<01:18,  2.02it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  91% 1544/1702 [12:43<01:18,  2.02it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  91% 1545/1702 [12:43<01:17,  2.02it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  91% 1546/1702 [12:43<01:17,  2.02it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  91% 1547/1702 [12:43<01:16,  2.03it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  91% 1548/1702 [12:44<01:16,  2.03it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  91% 1549/1702 [12:44<01:15,  2.03it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  91% 1550/1702 [12:44<01:14,  2.03it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  91% 1551/1702 [12:44<01:14,  2.03it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  91% 1552/1702 [12:44<01:13,  2.03it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  91% 1553/1702 [12:44<01:13,  2.03it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  91% 1554/1702 [12:45<01:12,  2.03it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  91% 1555/1702 [12:45<01:12,  2.03it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  91% 1556/1702 [12:45<01:11,  2.03it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  91% 1557/1702 [12:45<01:11,  2.03it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  92% 1558/1702 [12:45<01:10,  2.03it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  92% 1559/1702 [12:45<01:10,  2.04it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  92% 1560/1702 [12:46<01:09,  2.04it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  92% 1561/1702 [12:46<01:09,  2.04it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  92% 1562/1702 [12:46<01:08,  2.04it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  92% 1563/1702 [12:46<01:08,  2.04it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  92% 1564/1702 [12:46<01:07,  2.04it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  92% 1565/1702 [12:46<01:07,  2.04it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  92% 1566/1702 [12:47<01:06,  2.04it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  92% 1567/1702 [12:47<01:06,  2.04it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  92% 1568/1702 [12:47<01:05,  2.04it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  92% 1569/1702 [12:47<01:05,  2.04it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  92% 1570/1702 [12:47<01:04,  2.05it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  92% 1571/1702 [12:47<01:04,  2.05it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  92% 1572/1702 [12:48<01:03,  2.05it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  92% 1573/1702 [12:48<01:02,  2.05it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  92% 1574/1702 [12:48<01:02,  2.05it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  93% 1575/1702 [12:48<01:01,  2.05it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  93% 1576/1702 [12:48<01:01,  2.05it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  93% 1577/1702 [12:48<01:00,  2.05it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  93% 1578/1702 [12:49<01:00,  2.05it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  93% 1579/1702 [12:49<00:59,  2.05it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  93% 1580/1702 [12:49<00:59,  2.05it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  93% 1581/1702 [12:49<00:58,  2.05it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  93% 1582/1702 [12:49<00:58,  2.06it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  93% 1583/1702 [12:49<00:57,  2.06it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  93% 1584/1702 [12:49<00:57,  2.06it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  93% 1585/1702 [12:50<00:56,  2.06it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  93% 1586/1702 [12:50<00:56,  2.06it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  93% 1587/1702 [12:50<00:55,  2.06it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  93% 1588/1702 [12:50<00:55,  2.06it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  93% 1589/1702 [12:50<00:54,  2.06it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  93% 1590/1702 [12:50<00:54,  2.06it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  93% 1591/1702 [12:51<00:53,  2.06it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  94% 1592/1702 [12:51<00:53,  2.06it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  94% 1593/1702 [12:51<00:52,  2.06it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  94% 1594/1702 [12:51<00:52,  2.07it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  94% 1595/1702 [12:51<00:51,  2.07it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  94% 1596/1702 [12:51<00:51,  2.07it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  94% 1597/1702 [12:52<00:50,  2.07it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  94% 1598/1702 [12:52<00:50,  2.07it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  94% 1599/1702 [12:52<00:49,  2.07it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  94% 1600/1702 [12:52<00:49,  2.07it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  94% 1601/1702 [12:52<00:48,  2.07it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  94% 1602/1702 [12:52<00:48,  2.07it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  94% 1603/1702 [12:53<00:47,  2.07it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  94% 1604/1702 [12:53<00:47,  2.07it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  94% 1605/1702 [12:53<00:46,  2.08it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  94% 1606/1702 [12:53<00:46,  2.08it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  94% 1607/1702 [12:53<00:45,  2.08it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  94% 1608/1702 [12:53<00:45,  2.08it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  95% 1609/1702 [12:54<00:44,  2.08it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  95% 1610/1702 [12:54<00:44,  2.08it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  95% 1611/1702 [12:54<00:43,  2.08it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  95% 1612/1702 [12:54<00:43,  2.08it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  95% 1613/1702 [12:54<00:42,  2.08it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  95% 1614/1702 [12:54<00:42,  2.08it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  95% 1615/1702 [12:55<00:41,  2.08it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  95% 1616/1702 [12:55<00:41,  2.08it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  95% 1617/1702 [12:55<00:40,  2.09it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  95% 1618/1702 [12:55<00:40,  2.09it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  95% 1619/1702 [12:55<00:39,  2.09it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  95% 1620/1702 [12:55<00:39,  2.09it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  95% 1621/1702 [12:56<00:38,  2.09it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  95% 1622/1702 [12:56<00:38,  2.09it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  95% 1623/1702 [12:56<00:37,  2.09it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  95% 1624/1702 [12:56<00:37,  2.09it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  95% 1625/1702 [12:56<00:36,  2.09it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  96% 1626/1702 [12:56<00:36,  2.09it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  96% 1627/1702 [12:57<00:35,  2.09it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  96% 1628/1702 [12:57<00:35,  2.09it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  96% 1629/1702 [12:57<00:34,  2.10it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  96% 1630/1702 [12:57<00:34,  2.10it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  96% 1631/1702 [12:57<00:33,  2.10it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  96% 1632/1702 [12:57<00:33,  2.10it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  96% 1633/1702 [12:58<00:32,  2.10it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  96% 1634/1702 [12:58<00:32,  2.10it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  96% 1635/1702 [12:58<00:31,  2.10it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  96% 1636/1702 [12:58<00:31,  2.10it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  96% 1637/1702 [12:58<00:30,  2.10it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  96% 1638/1702 [12:58<00:30,  2.10it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  96% 1639/1702 [12:59<00:29,  2.10it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  96% 1640/1702 [12:59<00:29,  2.10it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  96% 1641/1702 [12:59<00:28,  2.11it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  96% 1642/1702 [12:59<00:28,  2.11it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  97% 1643/1702 [12:59<00:27,  2.11it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  97% 1644/1702 [12:59<00:27,  2.11it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  97% 1645/1702 [13:00<00:27,  2.11it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  97% 1646/1702 [13:00<00:26,  2.11it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  97% 1647/1702 [13:00<00:26,  2.11it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  97% 1648/1702 [13:00<00:25,  2.11it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  97% 1649/1702 [13:00<00:25,  2.11it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  97% 1650/1702 [13:00<00:24,  2.11it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  97% 1651/1702 [13:00<00:24,  2.11it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  97% 1652/1702 [13:01<00:23,  2.11it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  97% 1653/1702 [13:01<00:23,  2.12it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  97% 1654/1702 [13:01<00:22,  2.12it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  97% 1655/1702 [13:01<00:22,  2.12it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  97% 1656/1702 [13:01<00:21,  2.12it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  97% 1657/1702 [13:01<00:21,  2.12it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  97% 1658/1702 [13:02<00:20,  2.12it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  97% 1659/1702 [13:02<00:20,  2.12it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  98% 1660/1702 [13:02<00:19,  2.12it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  98% 1661/1702 [13:02<00:19,  2.12it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  98% 1662/1702 [13:02<00:18,  2.12it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  98% 1663/1702 [13:02<00:18,  2.12it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  98% 1664/1702 [13:03<00:17,  2.12it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  98% 1665/1702 [13:03<00:17,  2.13it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  98% 1666/1702 [13:03<00:16,  2.13it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  98% 1667/1702 [13:03<00:16,  2.13it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  98% 1668/1702 [13:03<00:15,  2.13it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  98% 1669/1702 [13:03<00:15,  2.13it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  98% 1670/1702 [13:04<00:15,  2.13it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  98% 1671/1702 [13:04<00:14,  2.13it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  98% 1672/1702 [13:04<00:14,  2.13it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  98% 1673/1702 [13:04<00:13,  2.13it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  98% 1674/1702 [13:04<00:13,  2.13it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  98% 1675/1702 [13:04<00:12,  2.13it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  98% 1676/1702 [13:05<00:12,  2.13it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  99% 1677/1702 [13:05<00:11,  2.14it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  99% 1678/1702 [13:05<00:11,  2.14it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  99% 1679/1702 [13:05<00:10,  2.14it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  99% 1680/1702 [13:05<00:10,  2.14it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  99% 1681/1702 [13:05<00:09,  2.14it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  99% 1682/1702 [13:06<00:09,  2.14it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  99% 1683/1702 [13:06<00:08,  2.14it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  99% 1684/1702 [13:06<00:08,  2.14it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  99% 1685/1702 [13:06<00:07,  2.14it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  99% 1686/1702 [13:06<00:07,  2.14it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  99% 1687/1702 [13:06<00:06,  2.14it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  99% 1688/1702 [13:07<00:06,  2.14it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  99% 1689/1702 [13:07<00:06,  2.15it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  99% 1690/1702 [13:07<00:05,  2.15it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  99% 1691/1702 [13:07<00:05,  2.15it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  99% 1692/1702 [13:07<00:04,  2.15it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0:  99% 1693/1702 [13:07<00:04,  2.15it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0: 100% 1694/1702 [13:08<00:03,  2.15it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0: 100% 1695/1702 [13:08<00:03,  2.15it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0: 100% 1696/1702 [13:08<00:02,  2.15it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0: 100% 1697/1702 [13:08<00:02,  2.15it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0: 100% 1698/1702 [13:08<00:01,  2.15it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0: 100% 1699/1702 [13:08<00:01,  2.15it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0: 100% 1700/1702 [13:09<00:00,  2.15it/s, loss=0.158, v_num=0, train_loss=0.149]
Epoch 0: 100% 1701/1702 [13:09<00:00,  2.16it/s, loss=0.158, v_num=0, train_loss=0.149]/usr/local/lib/python3.6/dist-packages/pytorch_lightning/utilities/distributed.py:37: RuntimeWarning: The metric you returned None must be a `torch.Tensor` instance, checkpoint not saved HINT: what is the value of loss in validation_epoch_end()?
  warnings.warn(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/pytorch_lightning/utilities/distributed.py:37: RuntimeWarning: Can save best model only with loss available, skipping.
  warnings.warn(*args, **kwargs)
Epoch 0: 100% 1702/1702 [13:09<00:00,  2.16it/s, loss=0.158, v_num=0, train_loss=0.149, valid_loss=0.162]
Epoch 1:  73% 1246/1702 [10:19<03:46,  2.01it/s, loss=0.161, v_num=0, train_loss=0.191, valid_loss=0.162]/usr/local/lib/python3.6/dist-packages/pytorch_lightning/utilities/distributed.py:37: UserWarning: Detected KeyboardInterrupt, attempting graceful shutdown...
  warnings.warn(*args, **kwargs)
Saving latest checkpoint..
Epoch 1:  73% 1246/1702 [10:19<03:46,  2.01it/s, loss=0.161, v_num=0, train_loss=0.191, valid_loss=0.162]
This issue can be reproduced in current stable and in 0.10.1rc1 also