2023-09-26 10:57:55,782 INFO [loop_base.py:460] Node[0] 0 / 1084
NCCL version 2.14.3+cuda11.6
2023-09-26 10:58:13,906 INFO [metric_updater.py:360] Node[0] Epoch[0] Step[19] GlobalStep[19] ganet_mixvargenet_culane: CulaneF1Score[0.0021]
2023-09-26 10:58:28,946 INFO [metric_updater.py:360] Node[0] Epoch[0] Step[39] GlobalStep[39] ganet_mixvargenet_culane: CulaneF1Score[0.0018]
2023-09-26 10:58:36,617 INFO [loop_base.py:460] Node[0] 50 / 1084
2023-09-26 10:58:44,154 INFO [metric_updater.py:360] Node[0] Epoch[0] Step[59] GlobalStep[59] ganet_mixvargenet_culane: CulaneF1Score[0.0012]
2023-09-26 10:58:58,464 INFO [metric_updater.py:360] Node[0] Epoch[0] Step[79] GlobalStep[79] ganet_mixvargenet_culane: CulaneF1Score[0.0013]
2023-09-26 10:59:13,423 INFO [metric_updater.py:360] Node[0] Epoch[0] Step[99] GlobalStep[99] ganet_mixvargenet_culane: CulaneF1Score[0.0013]
2023-09-26 10:59:13,424 INFO [loop_base.py:460] Node[0] 100 / 1084
2023-09-26 10:59:28,475 INFO [metric_updater.py:360] Node[0] Epoch[0] Step[119] GlobalStep[119] ganet_mixvargenet_culane: CulaneF1Score[0.0017]
2023-09-26 10:59:43,451 INFO [metric_updater.py:360] Node[0] Epoch[0] Step[139] GlobalStep[139] ganet_mixvargenet_culane: CulaneF1Score[0.0014]
