
=== Start adding workers ===
=> Add worker SGDMWorker(index=0, momentum=0.9)
=> Add worker SGDMWorker(index=1, momentum=0.9)
=> Add worker SGDMWorker(index=2, momentum=0.9)
=> Add worker SGDMWorker(index=3, momentum=0.9)
=> Add worker SGDMWorker(index=4, momentum=0.9)

=== Start adding graph ===
Ring(n=5)

Train epoch 1
[E 1B0  |    160/60000 (  0%) ] Loss: 2.3148 top1=  7.5000

=== Peeking data label distribution E1B0 ===
Worker 0 has targets: tensor([9, 4, 7, 0, 3], device='cuda:0')
Worker 1 has targets: tensor([3, 9, 4, 6, 1], device='cuda:0')
Worker 2 has targets: tensor([5, 6, 8, 8, 3], device='cuda:0')
Worker 3 has targets: tensor([4, 8, 9, 9, 2], device='cuda:0')
Worker 4 has targets: tensor([7, 8, 4, 9, 5], device='cuda:0')



=== Log mixing matrix @ E1B0 ===
[[0.333 0.333 0.    0.    0.333]
 [0.333 0.333 0.333 0.    0.   ]
 [0.    0.333 0.333 0.333 0.   ]
 [0.    0.    0.333 0.333 0.333]
 [0.333 0.    0.    0.333 0.333]]


[E 1B10 |   1760/60000 (  3%) ] Loss: 1.9207 top1= 45.0000
[E 1B20 |   3360/60000 (  6%) ] Loss: 0.8124 top1= 70.0000

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.4654 top1= 85.8574

Train epoch 2
[E 2B0  |    160/60000 (  0%) ] Loss: 0.7616 top1= 76.8750
[E 2B10 |   1760/60000 (  3%) ] Loss: 0.5259 top1= 81.8750
[E 2B20 |   3360/60000 (  6%) ] Loss: 0.3702 top1= 87.5000

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.3128 top1= 90.6751

Train epoch 3
[E 3B0  |    160/60000 (  0%) ] Loss: 0.3797 top1= 89.3750
[E 3B10 |   1760/60000 (  3%) ] Loss: 0.1807 top1= 95.0000
[E 3B20 |   3360/60000 (  6%) ] Loss: 0.1506 top1= 93.7500

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.2691 top1= 91.6767

Train epoch 4
[E 4B0  |    160/60000 (  0%) ] Loss: 0.1577 top1= 95.6250
[E 4B10 |   1760/60000 (  3%) ] Loss: 0.0741 top1= 98.1250
[E 4B20 |   3360/60000 (  6%) ] Loss: 0.0543 top1= 99.3750

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.2719 top1= 92.2175

Train epoch 5
[E 5B0  |    160/60000 (  0%) ] Loss: 0.0633 top1= 98.7500
[E 5B10 |   1760/60000 (  3%) ] Loss: 0.0220 top1=100.0000
[E 5B20 |   3360/60000 (  6%) ] Loss: 0.0330 top1= 99.3750

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.3024 top1= 92.0172

Train epoch 6
[E 6B0  |    160/60000 (  0%) ] Loss: 0.0330 top1= 99.3750
[E 6B10 |   1760/60000 (  3%) ] Loss: 0.0557 top1= 97.5000
[E 6B20 |   3360/60000 (  6%) ] Loss: 0.0179 top1=100.0000

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.3133 top1= 92.2175

Train epoch 7
[E 7B0  |    160/60000 (  0%) ] Loss: 0.0190 top1=100.0000
[E 7B10 |   1760/60000 (  3%) ] Loss: 0.0106 top1=100.0000
[E 7B20 |   3360/60000 (  6%) ] Loss: 0.0179 top1= 99.3750

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.2956 top1= 92.6482

Train epoch 8
[E 8B0  |    160/60000 (  0%) ] Loss: 0.0464 top1= 98.1250
[E 8B10 |   1760/60000 (  3%) ] Loss: 0.0267 top1= 99.3750
[E 8B20 |   3360/60000 (  6%) ] Loss: 0.0319 top1= 98.7500

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.2912 top1= 93.0389

Train epoch 9
[E 9B0  |    160/60000 (  0%) ] Loss: 0.0081 top1=100.0000
[E 9B10 |   1760/60000 (  3%) ] Loss: 0.0114 top1=100.0000
[E 9B20 |   3360/60000 (  6%) ] Loss: 0.0077 top1=100.0000

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.2966 top1= 93.1691

Train epoch 10
[E10B0  |    160/60000 (  0%) ] Loss: 0.0194 top1= 99.3750
[E10B10 |   1760/60000 (  3%) ] Loss: 0.0198 top1= 99.3750
[E10B20 |   3360/60000 (  6%) ] Loss: 0.0057 top1=100.0000

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.2982 top1= 93.3393

Train epoch 11
[E11B0  |    160/60000 (  0%) ] Loss: 0.0074 top1=100.0000
[E11B10 |   1760/60000 (  3%) ] Loss: 0.0045 top1=100.0000
[E11B20 |   3360/60000 (  6%) ] Loss: 0.0025 top1=100.0000

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.3040 top1= 93.2792

Train epoch 12
[E12B0  |    160/60000 (  0%) ] Loss: 0.0027 top1=100.0000
[E12B10 |   1760/60000 (  3%) ] Loss: 0.0023 top1=100.0000
[E12B20 |   3360/60000 (  6%) ] Loss: 0.0019 top1=100.0000

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.3081 top1= 93.4195

Train epoch 13
[E13B0  |    160/60000 (  0%) ] Loss: 0.0024 top1=100.0000
[E13B10 |   1760/60000 (  3%) ] Loss: 0.0011 top1=100.0000
[E13B20 |   3360/60000 (  6%) ] Loss: 0.0012 top1=100.0000

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.3082 top1= 93.5697

Train epoch 14
[E14B0  |    160/60000 (  0%) ] Loss: 0.0009 top1=100.0000
[E14B10 |   1760/60000 (  3%) ] Loss: 0.0008 top1=100.0000
[E14B20 |   3360/60000 (  6%) ] Loss: 0.0007 top1=100.0000

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.3138 top1= 93.5296

Train epoch 15
[E15B0  |    160/60000 (  0%) ] Loss: 0.0008 top1=100.0000
[E15B10 |   1760/60000 (  3%) ] Loss: 0.0007 top1=100.0000
[E15B20 |   3360/60000 (  6%) ] Loss: 0.0006 top1=100.0000

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.3174 top1= 93.5397

Train epoch 16
[E16B0  |    160/60000 (  0%) ] Loss: 0.0007 top1=100.0000
[E16B10 |   1760/60000 (  3%) ] Loss: 0.0006 top1=100.0000
[E16B20 |   3360/60000 (  6%) ] Loss: 0.0005 top1=100.0000

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.3209 top1= 93.5397

Train epoch 17
[E17B0  |    160/60000 (  0%) ] Loss: 0.0007 top1=100.0000
[E17B10 |   1760/60000 (  3%) ] Loss: 0.0005 top1=100.0000
[E17B20 |   3360/60000 (  6%) ] Loss: 0.0005 top1=100.0000

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.3240 top1= 93.5296

Train epoch 18
[E18B0  |    160/60000 (  0%) ] Loss: 0.0006 top1=100.0000
[E18B10 |   1760/60000 (  3%) ] Loss: 0.0005 top1=100.0000
[E18B20 |   3360/60000 (  6%) ] Loss: 0.0004 top1=100.0000

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.3268 top1= 93.5296

Train epoch 19
[E19B0  |    160/60000 (  0%) ] Loss: 0.0006 top1=100.0000
[E19B10 |   1760/60000 (  3%) ] Loss: 0.0004 top1=100.0000
[E19B20 |   3360/60000 (  6%) ] Loss: 0.0004 top1=100.0000

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.3294 top1= 93.5196

Train epoch 20
[E20B0  |    160/60000 (  0%) ] Loss: 0.0005 top1=100.0000
[E20B10 |   1760/60000 (  3%) ] Loss: 0.0004 top1=100.0000
[E20B20 |   3360/60000 (  6%) ] Loss: 0.0003 top1=100.0000

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.3318 top1= 93.5196

