
=== Start adding workers ===
{'Type': 'Setup', 'Dataset': 'cifar10', 'data_dir': '/home/he/dero/datasets/', 'train': True, 'download': True, 'batch_size': 64, 'shuffle': None, 'sampler': 'DistributedSampler(num_replicas=10,rank=0,shuffle=True)'}
=> Add worker SGDMWorker(index=0, momentum=0.9)
{'Type': 'Setup', 'Dataset': 'cifar10', 'data_dir': '/home/he/dero/datasets/', 'train': True, 'download': True, 'batch_size': 64, 'shuffle': None, 'sampler': 'DistributedSampler(num_replicas=10,rank=1,shuffle=True)'}
=> Add worker SGDMWorker(index=1, momentum=0.9)
{'Type': 'Setup', 'Dataset': 'cifar10', 'data_dir': '/home/he/dero/datasets/', 'train': True, 'download': True, 'batch_size': 64, 'shuffle': None, 'sampler': 'DistributedSampler(num_replicas=10,rank=2,shuffle=True)'}
=> Add worker SGDMWorker(index=2, momentum=0.9)
{'Type': 'Setup', 'Dataset': 'cifar10', 'data_dir': '/home/he/dero/datasets/', 'train': True, 'download': True, 'batch_size': 64, 'shuffle': None, 'sampler': 'DistributedSampler(num_replicas=10,rank=3,shuffle=True)'}
=> Add worker SGDMWorker(index=3, momentum=0.9)
{'Type': 'Setup', 'Dataset': 'cifar10', 'data_dir': '/home/he/dero/datasets/', 'train': True, 'download': True, 'batch_size': 64, 'shuffle': None, 'sampler': 'DistributedSampler(num_replicas=10,rank=4,shuffle=True)'}
=> Add worker SGDMWorker(index=4, momentum=0.9)
{'Type': 'Setup', 'Dataset': 'cifar10', 'data_dir': '/home/he/dero/datasets/', 'train': True, 'download': True, 'batch_size': 64, 'shuffle': None, 'sampler': 'DistributedSampler(num_replicas=10,rank=5,shuffle=True)'}
=> Add worker SGDMWorker(index=5, momentum=0.9)
{'Type': 'Setup', 'Dataset': 'cifar10', 'data_dir': '/home/he/dero/datasets/', 'train': True, 'download': True, 'batch_size': 64, 'shuffle': None, 'sampler': 'DistributedSampler(num_replicas=10,rank=6,shuffle=True)'}
=> Add worker SGDMWorker(index=6, momentum=0.9)
{'Type': 'Setup', 'Dataset': 'cifar10', 'data_dir': '/home/he/dero/datasets/', 'train': True, 'download': True, 'batch_size': 64, 'shuffle': None, 'sampler': 'DistributedSampler(num_replicas=10,rank=7,shuffle=True)'}
=> Add worker SGDMWorker(index=7, momentum=0.9)
{'Type': 'Setup', 'Dataset': 'cifar10', 'data_dir': '/home/he/dero/datasets/', 'train': True, 'download': True, 'batch_size': 64, 'shuffle': None, 'sampler': 'DistributedSampler(num_replicas=10,rank=8,shuffle=True)'}
=> Add worker SGDMWorker(index=8, momentum=0.9)
{'Type': 'Setup', 'Dataset': 'cifar10', 'data_dir': '/home/he/dero/datasets/', 'train': True, 'download': True, 'batch_size': 64, 'shuffle': None, 'sampler': 'DistributedSampler(num_replicas=10,rank=9,shuffle=True)'}
=> Add worker SGDMWorker(index=9, momentum=0.9)

=== Start adding graph ===
<codes.graph_utils.Dumbbell object at 0x7f7370ec7730>

{'Type': 'Setup', 'Dataset': 'cifar10', 'data_dir': '/home/he/dero/datasets/', 'train': False, 'download': True, 'batch_size': 128, 'shuffle': False, 'sampler': None}
Train epoch 1
[E 1B0  |    640/50000 (  1%) ] Loss: 2.3041 top1=  9.2188

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



=== Log mixing matrix @ E1B0 ===
[[0.167 0.167 0.167 0.167 0.167 0.    0.    0.    0.    0.167]
 [0.167 0.333 0.167 0.167 0.167 0.    0.    0.    0.    0.   ]
 [0.167 0.167 0.333 0.167 0.167 0.    0.    0.    0.    0.   ]
 [0.167 0.167 0.167 0.333 0.167 0.    0.    0.    0.    0.   ]
 [0.167 0.167 0.167 0.167 0.333 0.    0.    0.    0.    0.   ]
 [0.    0.    0.    0.    0.    0.333 0.167 0.167 0.167 0.167]
 [0.    0.    0.    0.    0.    0.167 0.333 0.167 0.167 0.167]
 [0.    0.    0.    0.    0.    0.167 0.167 0.333 0.167 0.167]
 [0.    0.    0.    0.    0.    0.167 0.167 0.167 0.333 0.167]
 [0.167 0.    0.    0.    0.    0.167 0.167 0.167 0.167 0.167]]


[E 1B10 |   7040/50000 ( 14%) ] Loss: 2.3016 top1= 11.5625
[E 1B20 |  13440/50000 ( 27%) ] Loss: 2.2978 top1=  9.5312
