
=== 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)
=> Add worker SGDMWorker(index=5, momentum=0.9)
=> Add worker SGDMWorker(index=6, momentum=0.9)
=> Add worker SGDMWorker(index=7, momentum=0.9)
=> Add worker SGDMWorker(index=8, momentum=0.9)
=> Add worker SGDMWorker(index=9, momentum=0.9)
=> Add worker SGDMWorker(index=10, momentum=0.9)
=> Add worker SGDMWorker(index=11, momentum=0.9)
=> Add worker SGDMWorker(index=12, momentum=0.9)
=> Add worker SGDMWorker(index=13, momentum=0.9)
=> Add worker SGDMWorker(index=14, momentum=0.9)
=> Add worker SGDMWorker(index=15, momentum=0.9)
=> Add worker SGDMWorker(index=16, momentum=0.9)
=> Add worker SGDMWorker(index=17, momentum=0.9)
=> Add worker SGDMWorker(index=18, momentum=0.9)
=> Add worker SGDMWorker(index=19, momentum=0.9)
=> Add worker ByzantineWorker(index=20)
=> Add worker ByzantineWorker(index=21)

=== Start adding graph ===
<codes.graph_utils.Dumbbell object at 0x7fa4b2bc96d0>

Train epoch 1
[E 1B0  |    704/60000 (  1%) ] Loss: 2.3066 top1=  9.2188

=== Peeking data label distribution E1B0 ===
Worker 0 has targets: tensor([3, 2, 2, 4, 3], device='cuda:0')
Worker 1 has targets: tensor([1, 0, 0, 4, 0], device='cuda:0')
Worker 2 has targets: tensor([4, 1, 0, 1, 0], device='cuda:0')
Worker 3 has targets: tensor([0, 1, 4, 1, 3], device='cuda:0')
Worker 4 has targets: tensor([0, 4, 1, 2, 4], device='cuda:0')
Worker 5 has targets: tensor([2, 2, 4, 4, 4], device='cuda:0')
Worker 6 has targets: tensor([1, 1, 4, 4, 3], device='cuda:0')
Worker 7 has targets: tensor([4, 4, 1, 3, 0], device='cuda:0')
Worker 8 has targets: tensor([1, 3, 1, 0, 4], device='cuda:0')
Worker 9 has targets: tensor([1, 3, 3, 3, 1], device='cuda:0')
Worker 10 has targets: tensor([8, 9, 7, 7, 9], device='cuda:0')
Worker 11 has targets: tensor([8, 9, 6, 6, 7], device='cuda:0')
Worker 12 has targets: tensor([8, 6, 5, 7, 8], device='cuda:0')
Worker 13 has targets: tensor([7, 6, 9, 6, 5], device='cuda:0')
Worker 14 has targets: tensor([8, 5, 8, 6, 7], device='cuda:0')
Worker 15 has targets: tensor([9, 5, 6, 8, 6], device='cuda:0')
Worker 16 has targets: tensor([7, 7, 8, 5, 8], device='cuda:0')
Worker 17 has targets: tensor([9, 7, 5, 6, 6], device='cuda:0')
Worker 18 has targets: tensor([7, 7, 7, 6, 6], device='cuda:0')
Worker 19 has targets: tensor([5, 7, 9, 9, 7], device='cuda:0')
Worker 20 has targets: tensor([3, 2, 2, 4, 3], device='cuda:0')
Worker 21 has targets: tensor([8, 9, 7, 7, 9], device='cuda:0')


[E 1B10 |   7744/60000 ( 13%) ] Loss: 1.0279 top1= 62.3438
[E 1B20 |  14784/60000 ( 25%) ] Loss: 0.4000 top1= 87.8125

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.7366 top1= 78.4555


=> Averaged model (Clique1 Average Validation Accuracy) | Eval Loss=9.1678 top1= 49.2889


=> Averaged model (Clique2 Average Validation Accuracy) | Eval Loss=7.8753 top1= 44.2708

Train epoch 2
[E 2B0  |    704/60000 (  1%) ] Loss: 0.2816 top1= 90.7812
[E 2B10 |   7744/60000 ( 13%) ] Loss: 0.1887 top1= 93.7500
[E 2B20 |  14784/60000 ( 25%) ] Loss: 0.1979 top1= 94.3750

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.6939 top1= 84.2748


=> Averaged model (Clique1 Average Validation Accuracy) | Eval Loss=5.6821 top1= 49.8197


=> Averaged model (Clique2 Average Validation Accuracy) | Eval Loss=5.7175 top1= 45.8634

Train epoch 3
[E 3B0  |    704/60000 (  1%) ] Loss: 0.1620 top1= 94.5312
[E 3B10 |   7744/60000 ( 13%) ] Loss: 0.1097 top1= 96.5625
[E 3B20 |  14784/60000 ( 25%) ] Loss: 0.1189 top1= 96.8750

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.6366 top1= 85.4567


=> Averaged model (Clique1 Average Validation Accuracy) | Eval Loss=5.7088 top1= 50.1302


=> Averaged model (Clique2 Average Validation Accuracy) | Eval Loss=5.8785 top1= 46.2039

Train epoch 4
[E 4B0  |    704/60000 (  1%) ] Loss: 0.1118 top1= 96.7188
[E 4B10 |   7744/60000 ( 13%) ] Loss: 0.0786 top1= 97.8125
[E 4B20 |  14784/60000 ( 25%) ] Loss: 0.0768 top1= 97.1875

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.5798 top1= 86.2881


=> Averaged model (Clique1 Average Validation Accuracy) | Eval Loss=6.0149 top1= 50.3906


=> Averaged model (Clique2 Average Validation Accuracy) | Eval Loss=6.0318 top1= 46.5345

Train epoch 5
[E 5B0  |    704/60000 (  1%) ] Loss: 0.0765 top1= 98.2812
[E 5B10 |   7744/60000 ( 13%) ] Loss: 0.0571 top1= 98.5938
[E 5B20 |  14784/60000 ( 25%) ] Loss: 0.0526 top1= 98.4375

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.5434 top1= 85.8574


=> Averaged model (Clique1 Average Validation Accuracy) | Eval Loss=6.2840 top1= 50.4708


=> Averaged model (Clique2 Average Validation Accuracy) | Eval Loss=6.2477 top1= 46.7348

Train epoch 6
[E 6B0  |    704/60000 (  1%) ] Loss: 0.0543 top1= 98.5938
[E 6B10 |   7744/60000 ( 13%) ] Loss: 0.0368 top1= 99.2188
[E 6B20 |  14784/60000 ( 25%) ] Loss: 0.0398 top1= 98.9062

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.5079 top1= 86.4283


=> Averaged model (Clique1 Average Validation Accuracy) | Eval Loss=6.5746 top1= 50.5108


=> Averaged model (Clique2 Average Validation Accuracy) | Eval Loss=6.3945 top1= 46.8650

Train epoch 7
[E 7B0  |    704/60000 (  1%) ] Loss: 0.0408 top1= 99.2188
[E 7B10 |   7744/60000 ( 13%) ] Loss: 0.0264 top1= 99.3750
[E 7B20 |  14784/60000 ( 25%) ] Loss: 0.0274 top1= 99.0625

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.4811 top1= 86.1879


=> Averaged model (Clique1 Average Validation Accuracy) | Eval Loss=6.9458 top1= 50.5509


=> Averaged model (Clique2 Average Validation Accuracy) | Eval Loss=6.8206 top1= 46.9151

Train epoch 8
[E 8B0  |    704/60000 (  1%) ] Loss: 0.0299 top1= 99.3750
[E 8B10 |   7744/60000 ( 13%) ] Loss: 0.0189 top1= 99.8438
[E 8B20 |  14784/60000 ( 25%) ] Loss: 0.0212 top1= 99.2188

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.4610 top1= 86.1579


=> Averaged model (Clique1 Average Validation Accuracy) | Eval Loss=7.0700 top1= 50.5909


=> Averaged model (Clique2 Average Validation Accuracy) | Eval Loss=7.4352 top1= 46.7748

Train epoch 9
[E 9B0  |    704/60000 (  1%) ] Loss: 0.0225 top1= 99.3750
[E 9B10 |   7744/60000 ( 13%) ] Loss: 0.0130 top1= 99.8438
[E 9B20 |  14784/60000 ( 25%) ] Loss: 0.0216 top1= 99.2188

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.4753 top1= 84.9159


=> Averaged model (Clique1 Average Validation Accuracy) | Eval Loss=7.1175 top1= 50.6711


=> Averaged model (Clique2 Average Validation Accuracy) | Eval Loss=7.8441 top1= 46.9451

Train epoch 10
[E10B0  |    704/60000 (  1%) ] Loss: 0.0149 top1= 99.5312
[E10B10 |   7744/60000 ( 13%) ] Loss: 0.0108 top1= 99.8438
[E10B20 |  14784/60000 ( 25%) ] Loss: 0.0103 top1= 99.6875

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.4748 top1= 84.7756


=> Averaged model (Clique1 Average Validation Accuracy) | Eval Loss=7.3377 top1= 50.6110


=> Averaged model (Clique2 Average Validation Accuracy) | Eval Loss=8.1823 top1= 46.8650

Train epoch 11
[E11B0  |    704/60000 (  1%) ] Loss: 0.0136 top1= 99.6875
[E11B10 |   7744/60000 ( 13%) ] Loss: 0.0102 top1= 99.8438
[E11B20 |  14784/60000 ( 25%) ] Loss: 0.0102 top1=100.0000

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.4431 top1= 86.1278


=> Averaged model (Clique1 Average Validation Accuracy) | Eval Loss=7.3611 top1= 50.5909


=> Averaged model (Clique2 Average Validation Accuracy) | Eval Loss=8.3670 top1= 47.0954

Train epoch 12
[E12B0  |    704/60000 (  1%) ] Loss: 0.0077 top1= 99.8438
[E12B10 |   7744/60000 ( 13%) ] Loss: 0.0080 top1= 99.8438
[E12B20 |  14784/60000 ( 25%) ] Loss: 0.0101 top1= 99.6875

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=0.4277 top1= 86.4483


=> Averaged model (Clique1 Average Validation Accuracy) | Eval Loss=7.5027 top1= 50.6510


=> Averaged model (Clique2 Average Validation Accuracy) | Eval Loss=8.3364 top1= 47.1154

Train epoch 13
[E13B0  |    704/60000 (  1%) ] Loss: 0.0045 top1=100.0000
[E13B10 |   7744/60000 ( 13%) ] Loss: 0.0029 top1=100.0000
