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[2023-12-25 09:03:53,677][distributed_c10d.py][INFO] Added key: store_based_barrier_key:1 to store for rank: 5
[2023-12-25 09:03:53,687][distributed_c10d.py][INFO] Rank 4: Completed store-based barrier for key:store_based_barrier_key:1 with 8 nodes.
[2023-12-25 09:04:20,079][model5_pretrain.py][INFO] Epoch:[0/1](0/112548) loss:11.448 lr:0.0000000 epoch_Time:5160.0min:
[2023-12-25 09:04:20,082][distributed.py][INFO] Reducer buckets have been rebuilt in this iteration.
[2023-12-25 09:04:58,031][model5_pretrain.py][INFO] Epoch:[0/1](100/112548) loss:7.868 lr:0.0000300 epoch_Time:755.0min:
[2023-12-25 09:05:35,666][model5_pretrain.py][INFO] Epoch:[0/1](200/112548) loss:7.416 lr:0.0000600 epoch_Time:730.0min:
[2023-12-25 09:06:13,474][model5_pretrain.py][INFO] Epoch:[0/1](300/112548) loss:6.188 lr:0.0000900 epoch_Time:723.0min:
[2023-12-25 09:06:51,226][model5_pretrain.py][INFO] Epoch:[0/1](400/112548) loss:6.891 lr:0.0001200 epoch_Time:717.0min:
[2023-12-25 09:07:28,887][model5_pretrain.py][INFO] Epoch:[0/1](500/112548) loss:5.332 lr:0.0001500 epoch_Time:714.0min:
[2023-12-25 09:08:06,560][model5_pretrain.py][INFO] Epoch:[0/1](600/112548) loss:5.096 lr:0.0001800 epoch_Time:712.0min:
[2023-12-25 09:08:44,245][model5_pretrain.py][INFO] Epoch:[0/1](700/112548) loss:5.120 lr:0.0002100 epoch_Time:710.0min:
[2023-12-25 09:09:21,965][model5_pretrain.py][INFO] Epoch:[0/1](800/112548) loss:4.798 lr:0.0002400 epoch_Time:708.0min:
[2023-12-25 09:09:59,676][model5_pretrain.py][INFO] Epoch:[0/1](900/112548) loss:4.646 lr:0.0002700 epoch_Time:707.0min:
[2023-12-25 09:10:37,397][model5_pretrain.py][INFO] Epoch:[0/1](1000/112548) loss:4.587 lr:0.0003000 epoch_Time:706.0min:
[2023-12-25 09:11:15,085][model5_pretrain.py][INFO] Epoch:[0/1](1100/112548) loss:4.548 lr:0.0003000 epoch_Time:705.0min:
[2023-12-25 09:11:52,785][model5_pretrain.py][INFO] Epoch:[0/1](1200/112548) loss:4.478 lr:0.0003000 epoch_Time:704.0min:
[2023-12-25 09:12:30,484][model5_pretrain.py][INFO] Epoch:[0/1](1300/112548) loss:4.533 lr:0.0003000 epoch_Time:703.0min:
[2023-12-25 09:13:08,204][model5_pretrain.py][INFO] Epoch:[0/1](1400/112548) loss:4.661 lr:0.0003000 epoch_Time:702.0min:
[2023-12-25 09:13:45,901][model5_pretrain.py][INFO] Epoch:[0/1](1500/112548) loss:4.775 lr:0.0003000 epoch_Time:701.0min:
[2023-12-25 09:14:23,591][model5_pretrain.py][INFO] Epoch:[0/1](1600/112548) loss:4.498 lr:0.0003000 epoch_Time:700.0min:
[2023-12-25 09:15:01,277][model5_pretrain.py][INFO] Epoch:[0/1](1700/112548) loss:3.631 lr:0.0002999 epoch_Time:700.0min:
[2023-12-25 09:15:38,957][model5_pretrain.py][INFO] Epoch:[0/1](1800/112548) loss:3.503 lr:0.0002999 epoch_Time:698.0min:
[2023-12-25 09:16:16,636][model5_pretrain.py][INFO] Epoch:[0/1](1900/112548) loss:4.775 lr:0.0002999 epoch_Time:698.0min:
[2023-12-25 09:16:54,324][model5_pretrain.py][INFO] Epoch:[0/1](2000/112548) loss:4.013 lr:0.0002999 epoch_Time:697.0min:
[2023-12-25 09:17:31,998][model5_pretrain.py][INFO] Epoch:[0/1](2100/112548) loss:3.858 lr:0.0002999 epoch_Time:696.0min:
[2023-12-25 09:18:09,678][model5_pretrain.py][INFO] Epoch:[0/1](2200/112548) loss:3.645 lr:0.0002998 epoch_Time:696.0min:
[2023-12-25 09:18:47,351][model5_pretrain.py][INFO] Epoch:[0/1](2300/112548) loss:4.378 lr:0.0002998 epoch_Time:695.0min:
[2023-12-25 09:19:25,021][model5_pretrain.py][INFO] Epoch:[0/1](2400/112548) loss:3.961 lr:0.0002998 epoch_Time:694.0min:
[2023-12-25 09:20:02,689][model5_pretrain.py][INFO] Epoch:[0/1](2500/112548) loss:4.001 lr:0.0002997 epoch_Time:694.0min:
[2023-12-25 09:20:40,352][model5_pretrain.py][INFO] Epoch:[0/1](2600/112548) loss:4.306 lr:0.0002997 epoch_Time:692.0min:
[2023-12-25 09:21:18,035][model5_pretrain.py][INFO] Epoch:[0/1](2700/112548) loss:4.191 lr:0.0002997 epoch_Time:691.0min:
[2023-12-25 09:21:55,707][model5_pretrain.py][INFO] Epoch:[0/1](2800/112548) loss:3.702 lr:0.0002996 epoch_Time:691.0min:
[2023-12-25 09:22:33,376][model5_pretrain.py][INFO] Epoch:[0/1](2900/112548) loss:4.223 lr:0.0002996 epoch_Time:690.0min:
[2023-12-25 09:23:11,050][model5_pretrain.py][INFO] Epoch:[0/1](3000/112548) loss:4.147 lr:0.0002995 epoch_Time:690.0min:
[2023-12-25 09:23:48,725][model5_pretrain.py][INFO] Epoch:[0/1](3100/112548) loss:4.070 lr:0.0002995 epoch_Time:689.0min:
[2023-12-25 09:24:26,396][model5_pretrain.py][INFO] Epoch:[0/1](3200/112548) loss:3.090 lr:0.0002994 epoch_Time:688.0min:
[2023-12-25 09:25:04,078][model5_pretrain.py][INFO] Epoch:[0/1](3300/112548) loss:3.437 lr:0.0002994 epoch_Time:688.0min:
[2023-12-25 09:25:41,760][model5_pretrain.py][INFO] Epoch:[0/1](3400/112548) loss:3.836 lr:0.0002993 epoch_Time:687.0min:
[2023-12-25 09:26:19,442][model5_pretrain.py][INFO] Epoch:[0/1](3500/112548) loss:4.028 lr:0.0002993 epoch_Time:686.0min:
[2023-12-25 09:26:57,130][model5_pretrain.py][INFO] Epoch:[0/1](3600/112548) loss:3.261 lr:0.0002992 epoch_Time:686.0min:
[2023-12-25 09:27:34,779][model5_pretrain.py][INFO] Epoch:[0/1](3700/112548) loss:4.117 lr:0.0002992 epoch_Time:685.0min:
[2023-12-25 09:28:12,457][model5_pretrain.py][INFO] Epoch:[0/1](3800/112548) loss:4.117 lr:0.0002991 epoch_Time:685.0min:
[2023-12-25 09:28:50,123][model5_pretrain.py][INFO] Epoch:[0/1](3900/112548) loss:3.766 lr:0.0002990 epoch_Time:684.0min:
[2023-12-25 09:29:27,785][model5_pretrain.py][INFO] Epoch:[0/1](4000/112548) loss:3.485 lr:0.0002990 epoch_Time:683.0min:
[2023-12-25 09:30:05,463][model5_pretrain.py][INFO] Epoch:[0/1](4100/112548) loss:3.641 lr:0.0002989 epoch_Time:683.0min:
[2023-12-25 09:30:43,127][model5_pretrain.py][INFO] Epoch:[0/1](4200/112548) loss:3.077 lr:0.0002988 epoch_Time:682.0min:
[2023-12-25 09:31:20,794][model5_pretrain.py][INFO] Epoch:[0/1](4300/112548) loss:3.516 lr:0.0002988 epoch_Time:681.0min:
[2023-12-25 09:31:58,473][model5_pretrain.py][INFO] Epoch:[0/1](4400/112548) loss:3.751 lr:0.0002987 epoch_Time:681.0min:
[2023-12-25 09:32:36,140][model5_pretrain.py][INFO] Epoch:[0/1](4500/112548) loss:3.204 lr:0.0002986 epoch_Time:679.0min:
[2023-12-25 09:33:13,808][model5_pretrain.py][INFO] Epoch:[0/1](4600/112548) loss:3.512 lr:0.0002985 epoch_Time:679.0min:
[2023-12-25 09:33:51,469][model5_pretrain.py][INFO] Epoch:[0/1](4700/112548) loss:3.664 lr:0.0002984 epoch_Time:678.0min:
[2023-12-25 09:34:29,147][model5_pretrain.py][INFO] Epoch:[0/1](4800/112548) loss:3.222 lr:0.0002983 epoch_Time:677.0min:
[2023-12-25 09:35:06,817][model5_pretrain.py][INFO] Epoch:[0/1](4900/112548) loss:3.977 lr:0.0002983 epoch_Time:677.0min:
[2023-12-25 09:35:44,496][model5_pretrain.py][INFO] Epoch:[0/1](5000/112548) loss:3.618 lr:0.0002982 epoch_Time:676.0min:
[2023-12-25 09:36:22,163][model5_pretrain.py][INFO] Epoch:[0/1](5100/112548) loss:3.887 lr:0.0002981 epoch_Time:675.0min:
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112100/112548) loss:2.652 lr:0.0000100 epoch_Time:2.0min:
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