
=== 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: 2.3020 top1= 11.2500
[E 1B20 |   3360/60000 (  6%) ] Loss: 2.3031 top1= 10.6250

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=2.3019 top1= 11.3482

Train epoch 2
[E 2B0  |    160/60000 (  0%) ] Loss: 2.3021 top1= 10.0000
[E 2B10 |   1760/60000 (  3%) ] Loss: 2.2992 top1= 11.2500
[E 2B20 |   3360/60000 (  6%) ] Loss: 2.3041 top1= 10.6250

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=2.3015 top1= 11.3482

Train epoch 3
[E 3B0  |    160/60000 (  0%) ] Loss: 2.3021 top1= 10.0000
[E 3B10 |   1760/60000 (  3%) ] Loss: 2.2971 top1= 11.2500
[E 3B20 |   3360/60000 (  6%) ] Loss: 2.3050 top1= 10.6250

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=2.3014 top1= 11.3482

Train epoch 4
[E 4B0  |    160/60000 (  0%) ] Loss: 2.3022 top1= 10.0000
[E 4B10 |   1760/60000 (  3%) ] Loss: 2.2958 top1= 11.2500
[E 4B20 |   3360/60000 (  6%) ] Loss: 2.3058 top1= 10.6250

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=2.3014 top1= 11.3482

Train epoch 5
[E 5B0  |    160/60000 (  0%) ] Loss: 2.3024 top1= 10.0000
[E 5B10 |   1760/60000 (  3%) ] Loss: 2.2949 top1= 11.2500
[E 5B20 |   3360/60000 (  6%) ] Loss: 2.3064 top1= 10.6250

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=2.3014 top1= 11.3482

Train epoch 6
[E 6B0  |    160/60000 (  0%) ] Loss: 2.3026 top1= 10.0000
[E 6B10 |   1760/60000 (  3%) ] Loss: 2.2943 top1= 11.2500
[E 6B20 |   3360/60000 (  6%) ] Loss: 2.3068 top1= 10.6250

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=2.3015 top1= 11.3482

Train epoch 7
[E 7B0  |    160/60000 (  0%) ] Loss: 2.3028 top1= 10.0000
[E 7B10 |   1760/60000 (  3%) ] Loss: 2.2938 top1= 11.2500
[E 7B20 |   3360/60000 (  6%) ] Loss: 2.3072 top1= 10.6250

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=2.3015 top1= 11.3482

Train epoch 8
[E 8B0  |    160/60000 (  0%) ] Loss: 2.3029 top1= 10.0000
[E 8B10 |   1760/60000 (  3%) ] Loss: 2.2935 top1= 11.2500
[E 8B20 |   3360/60000 (  6%) ] Loss: 2.3074 top1= 10.6250

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=2.3016 top1= 11.3482

Train epoch 9
[E 9B0  |    160/60000 (  0%) ] Loss: 2.3030 top1= 10.0000
[E 9B10 |   1760/60000 (  3%) ] Loss: 2.2933 top1= 11.2500
[E 9B20 |   3360/60000 (  6%) ] Loss: 2.3076 top1= 10.6250

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=2.3016 top1= 11.3482

Train epoch 10
[E10B0  |    160/60000 (  0%) ] Loss: 2.3030 top1= 10.0000
[E10B10 |   1760/60000 (  3%) ] Loss: 2.2932 top1= 11.2500
[E10B20 |   3360/60000 (  6%) ] Loss: 2.3077 top1= 10.6250

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=2.3016 top1= 11.3482

Train epoch 11
[E11B0  |    160/60000 (  0%) ] Loss: 2.3031 top1= 10.0000
[E11B10 |   1760/60000 (  3%) ] Loss: 2.2931 top1= 11.2500
[E11B20 |   3360/60000 (  6%) ] Loss: 2.3078 top1= 10.6250

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=2.3017 top1= 11.3482

Train epoch 12
[E12B0  |    160/60000 (  0%) ] Loss: 2.3031 top1= 10.0000
[E12B10 |   1760/60000 (  3%) ] Loss: 2.2930 top1= 11.2500
[E12B20 |   3360/60000 (  6%) ] Loss: 2.3079 top1= 10.6250

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=2.3017 top1= 11.3482

Train epoch 13
[E13B0  |    160/60000 (  0%) ] Loss: 2.3031 top1= 10.0000
[E13B10 |   1760/60000 (  3%) ] Loss: 2.2929 top1= 11.2500
[E13B20 |   3360/60000 (  6%) ] Loss: 2.3079 top1= 10.6250

=> Averaged model (Global Average Validation Accuracy) | Eval Loss=2.3017 top1= 11.3482

Train epoch 14
[E14B0  |    160/60000 (  0%) ] Loss: 2.3031 top1= 10.0000
[E14B10 |   1760/60000 (  3%) ] Loss: 2.2929 top1= 11.2500
