TACC:  Starting up job 3498327 
TACC:  Starting parallel tasks... 
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process rank 0 is bound to device 0
distributed environment is initialzied
model is created
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Files already downloaded and verified
training and testing dataloaders are created
loss is created
optimizer is created
start training
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process rank 2 is bound to device 2
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Files already downloaded and verified
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process rank 3 is bound to device 3
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process rank 4 is bound to device 0
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process rank 5 is bound to device 1
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process rank 7 is bound to device 3
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process rank 6 is bound to device 2
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warning: variables which starts with __, is a module or class declaration are omitted
process rank 1 is bound to device 1
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Files already downloaded and verified
epoch: 0, train loss: 2.1005014667705613
epoch: 1, train loss: 1.8539113086097094
epoch: 1, eval loss: 1.7973519027233125, correct: 3362, total: 10000, acc = 0.3361999988555908
epoch: 2, train loss: 1.7149482040989155
epoch: 3, train loss: 1.5927067617980801
epoch: 3, eval loss: 1.5848429083824158, correct: 4344, total: 10000, acc = 0.4343999922275543
epoch: 4, train loss: 1.4912729798531046
epoch: 5, train loss: 1.3957378158763962
epoch: 5, eval loss: 1.4951884388923644, correct: 4841, total: 10000, acc = 0.48409998416900635
epoch: 6, train loss: 1.3090402642074896
epoch: 7, train loss: 1.2566283296565621
epoch: 7, eval loss: 1.2464738070964814, correct: 5562, total: 10000, acc = 0.5561999678611755
epoch: 8, train loss: 1.2084139476017075
epoch: 9, train loss: 1.1706127719003327
epoch: 9, eval loss: 1.162048089504242, correct: 5876, total: 10000, acc = 0.5875999927520752
epoch: 10, train loss: 1.120817175933293
epoch: 11, train loss: 1.084984731309268
epoch: 11, eval loss: 1.0764922022819519, correct: 6155, total: 10000, acc = 0.6154999732971191
epoch: 12, train loss: 1.0559214432628787
epoch: 13, train loss: 1.0261321286765896
epoch: 13, eval loss: 1.0338306188583375, correct: 6334, total: 10000, acc = 0.6333999633789062
epoch: 14, train loss: 0.992842432187528
epoch: 15, train loss: 0.9660871296512837
epoch: 15, eval loss: 1.0059030145406722, correct: 6458, total: 10000, acc = 0.645799994468689
epoch: 16, train loss: 0.9467733100968965
epoch: 17, train loss: 0.9243187673237859
epoch: 17, eval loss: 0.9469569176435471, correct: 6610, total: 10000, acc = 0.6609999537467957
epoch: 18, train loss: 0.9059403721167116
epoch: 19, train loss: 0.8819177935318071
epoch: 19, eval loss: 0.9196836709976196, correct: 6727, total: 10000, acc = 0.6726999878883362
epoch: 20, train loss: 0.8721987532109631
epoch: 21, train loss: 0.8469706013494608
epoch: 21, eval loss: 0.8634845405817032, correct: 6976, total: 10000, acc = 0.6976000070571899
epoch: 22, train loss: 0.8352831839298716
epoch: 23, train loss: 0.8124590455269327
epoch: 23, eval loss: 0.8418784946203232, correct: 7034, total: 10000, acc = 0.7033999562263489
epoch: 24, train loss: 0.7961219853284408
epoch: 25, train loss: 0.7883704268202489
epoch: 25, eval loss: 0.8191130340099335, correct: 7116, total: 10000, acc = 0.7116000056266785
epoch: 26, train loss: 0.7733409623710477
epoch: 27, train loss: 0.7561956893424598
epoch: 27, eval loss: 0.8028618812561035, correct: 7200, total: 10000, acc = 0.7199999690055847
epoch: 28, train loss: 0.7479740460308231
epoch: 29, train loss: 0.7343520899208225
epoch: 29, eval loss: 0.7829996794462204, correct: 7256, total: 10000, acc = 0.725600004196167
epoch: 30, train loss: 0.7244430549290716
epoch: 31, train loss: 0.7121965617549663
epoch: 31, eval loss: 0.765428164601326, correct: 7299, total: 10000, acc = 0.7299000024795532
epoch: 32, train loss: 0.6988190838268825
epoch: 33, train loss: 0.6908610359746583
epoch: 33, eval loss: 0.7602580636739731, correct: 7395, total: 10000, acc = 0.7394999861717224
epoch: 34, train loss: 0.6785666395206841
epoch: 35, train loss: 0.6664504153387887
epoch: 35, eval loss: 0.7671193510293961, correct: 7345, total: 10000, acc = 0.734499990940094
epoch: 36, train loss: 0.6639333245705585
epoch: 37, train loss: 0.6509425913800999
epoch: 37, eval loss: 0.7612941324710846, correct: 7382, total: 10000, acc = 0.7382000088691711
epoch: 38, train loss: 0.6416311720196082
epoch: 39, train loss: 0.6312643265237614
epoch: 39, eval loss: 0.7380059510469437, correct: 7496, total: 10000, acc = 0.7495999932289124
epoch: 40, train loss: 0.620578939209179
epoch: 41, train loss: 0.6195461816933691
epoch: 41, eval loss: 0.7172901630401611, correct: 7550, total: 10000, acc = 0.7549999952316284
epoch: 42, train loss: 0.6013389248020795
epoch: 43, train loss: 0.6049416010477104
epoch: 43, eval loss: 0.7145429253578186, correct: 7569, total: 10000, acc = 0.7568999528884888
epoch: 44, train loss: 0.5950779300563189
epoch: 45, train loss: 0.5786038743598121
epoch: 45, eval loss: 0.7171747118234635, correct: 7569, total: 10000, acc = 0.7568999528884888
epoch: 46, train loss: 0.5752052083915594
epoch: 47, train loss: 0.5669339743195748
epoch: 47, eval loss: 0.7040806382894516, correct: 7601, total: 10000, acc = 0.7601000070571899
epoch: 48, train loss: 0.5596802952338238
epoch: 49, train loss: 0.5521421706189915
epoch: 49, eval loss: 0.7221358746290207, correct: 7592, total: 10000, acc = 0.7591999769210815
epoch: 50, train loss: 0.5504364164508119
epoch: 51, train loss: 0.5363630725412952
epoch: 51, eval loss: 0.710089972615242, correct: 7650, total: 10000, acc = 0.7649999856948853
epoch: 52, train loss: 0.5382009008709265
epoch: 53, train loss: 0.5292040118757559
epoch: 53, eval loss: 0.7044323921203614, correct: 7672, total: 10000, acc = 0.7671999931335449
epoch: 54, train loss: 0.5289747638970005
epoch: 55, train loss: 0.5239191630056926
epoch: 55, eval loss: 0.6983724802732467, correct: 7694, total: 10000, acc = 0.7694000005722046
epoch: 56, train loss: 0.5177402243930467
epoch: 57, train loss: 0.5132759012738053
epoch: 57, eval loss: 0.7066506981849671, correct: 7671, total: 10000, acc = 0.7670999765396118
epoch: 58, train loss: 0.5119742675095188
epoch: 59, train loss: 0.5074386891661858
epoch: 59, eval loss: 0.7012903690338135, correct: 7693, total: 10000, acc = 0.7692999839782715
finish training
