C:\Users\levi\AppData\Local\Microsoft\WindowsApps\python3.12.exe C:\Users\levi\Desktop\总目录-兆衍\跑分\pytorch-gpu-benchmark-main\benchmark_models.py
benchmark start : 2024/09/24 16:07:45
Number of GPUs on current device : 1
CUDA Version : 11.8
Cudnn Version : 90100
Device Name : Tesla V100-SXM2-32GB
uname_result(system='Windows', node='DESKTOP-V8VCGPS', release='10', version='10.0.19045', machine='AMD64')
scpufreq(current=2300.0, min=0.0, max=2300.0)
cpu_count: 8
memory_available: 1331605504
Benchmarking Training float precision type mnasnet0_5
mnasnet0_5 model average train time : 40.779852867126465ms
Benchmarking Training float precision type mnasnet0_75
mnasnet0_75 model average train time : 57.56028652191162ms
Benchmarking Training float precision type mnasnet1_0
mnasnet1_0 model average train time : 62.10038185119629ms
Benchmarking Training float precision type mnasnet1_3
mnasnet1_3 model average train time : 60.16016960144043ms
Benchmarking Training float precision type resnet18
resnet18 model average train time : 32.97965049743652ms
Benchmarking Training float precision type resnet34
resnet34 model average train time : 49.080657958984375ms
Benchmarking Training float precision type resnet50
resnet50 model average train time : 58.658742904663086ms
Benchmarking Training float precision type resnet101
resnet101 model average train time : 95.45299053192139ms
Benchmarking Training float precision type resnet152
resnet152 model average train time : 118.63776206970215ms
Benchmarking Training float precision type resnext50_32x4d
resnext50_32x4d model average train time : 94.1808557510376ms
Benchmarking Training float precision type resnext101_32x8d
resnext101_32x8d model average train time : 247.60605335235596ms
Benchmarking Training float precision type resnext101_64x4d
resnext101_64x4d model average train time : 263.53647232055664ms
Benchmarking Training float precision type wide_resnet50_2
wide_resnet50_2 model average train time : 102.8178882598877ms
Benchmarking Training float precision type wide_resnet101_2
wide_resnet101_2 model average train time : 169.45948123931885ms
Benchmarking Training float precision type densenet121
densenet121 model average train time : 70.9781551361084ms
Benchmarking Training float precision type densenet161
densenet161 model average train time : 138.28054904937744ms
Benchmarking Training float precision type densenet169
densenet169 model average train time : 94.05195236206055ms
Benchmarking Training float precision type densenet201
densenet201 model average train time : 118.00044059753418ms
Benchmarking Training float precision type squeezenet1_0
squeezenet1_0 model average train time : 24.398889541625977ms
Benchmarking Training float precision type squeezenet1_1
squeezenet1_1 model average train time : 23.419828414916992ms
Benchmarking Training float precision type vgg11
vgg11 model average train time : 52.15620517730713ms
Benchmarking Training float precision type vgg11_bn
vgg11_bn model average train time : 57.5331974029541ms
Benchmarking Training float precision type vgg13
vgg13 model average train time : 64.07845973968506ms
Benchmarking Training float precision type vgg13_bn
vgg13_bn model average train time : 75.23913860321045ms
Benchmarking Training float precision type vgg16
vgg16 model average train time : 79.0393877029419ms
Benchmarking Training float precision type vgg16_bn
vgg16_bn model average train time : 84.83956813812256ms
Benchmarking Training float precision type vgg19
vgg19 model average train time : 72.85277843475342ms
Benchmarking Training float precision type vgg19_bn
vgg19_bn model average train time : 81.79934024810791ms
Benchmarking Training float precision type mobilenet_v3_large
mobilenet_v3_large model average train time : 21.939353942871094ms
Benchmarking Training float precision type mobilenet_v3_small
mobilenet_v3_small model average train time : 15.919809341430664ms
Benchmarking Training float precision type shufflenet_v2_x0_5
shufflenet_v2_x0_5 model average train time : 19.15968894958496ms
Benchmarking Training float precision type shufflenet_v2_x1_0
shufflenet_v2_x1_0 model average train time : 21.83973789215088ms
Benchmarking Training float precision type shufflenet_v2_x1_5
shufflenet_v2_x1_5 model average train time : 19.1195011138916ms
Benchmarking Training float precision type shufflenet_v2_x2_0
shufflenet_v2_x2_0 model average train time : 22.25897789001465ms
Benchmarking Inference float precision type mnasnet0_5
mnasnet0_5 model average inference time : 8.699288368225098ms
Benchmarking Inference float precision type mnasnet0_75
mnasnet0_75 model average inference time : 9.519319534301758ms
Benchmarking Inference float precision type mnasnet1_0
mnasnet1_0 model average inference time : 18.99911403656006ms
Benchmarking Inference float precision type mnasnet1_3
mnasnet1_3 model average inference time : 22.16010093688965ms
Benchmarking Inference float precision type resnet18
resnet18 model average inference time : 22.259244918823242ms
Benchmarking Inference float precision type resnet34
resnet34 model average inference time : 21.059303283691406ms
Benchmarking Inference float precision type resnet50
resnet50 model average inference time : 27.0988130569458ms
Benchmarking Inference float precision type resnet101
resnet101 model average inference time : 36.17978096008301ms
Benchmarking Inference float precision type resnet152
resnet152 model average inference time : 45.93863487243652ms
Benchmarking Inference float precision type resnext50_32x4d
resnext50_32x4d model average inference time : 31.87857151031494ms
Benchmarking Inference float precision type resnext101_32x8d
resnext101_32x8d model average inference time : 76.87961101531982ms
Benchmarking Inference float precision type resnext101_64x4d
resnext101_64x4d model average inference time : 79.81907844543457ms
Benchmarking Inference float precision type wide_resnet50_2
wide_resnet50_2 model average inference time : 36.97860240936279ms
Benchmarking Inference float precision type wide_resnet101_2
wide_resnet101_2 model average inference time : 59.099273681640625ms
Benchmarking Inference float precision type densenet121
densenet121 model average inference time : 31.83908462524414ms
Benchmarking Inference float precision type densenet161
densenet161 model average inference time : 50.73944568634033ms
Benchmarking Inference float precision type densenet169
densenet169 model average inference time : 39.839816093444824ms
Benchmarking Inference float precision type densenet201
densenet201 model average inference time : 34.97932434082031ms
Benchmarking Inference float precision type squeezenet1_0
squeezenet1_0 model average inference time : 13.399724960327148ms
Benchmarking Inference float precision type squeezenet1_1
squeezenet1_1 model average inference time : 14.160284996032715ms
Benchmarking Inference float precision type vgg11
vgg11 model average inference time : 17.058839797973633ms
Benchmarking Inference float precision type vgg11_bn
vgg11_bn model average inference time : 27.99907684326172ms
Benchmarking Inference float precision type vgg13
vgg13 model average inference time : 23.240089416503906ms
Benchmarking Inference float precision type vgg13_bn
vgg13_bn model average inference time : 27.69937038421631ms
Benchmarking Inference float precision type vgg16
vgg16 model average inference time : 24.059300422668457ms
Benchmarking Inference float precision type vgg16_bn
vgg16_bn model average inference time : 37.35865116119385ms
Benchmarking Inference float precision type vgg19
vgg19 model average inference time : 28.23927879333496ms
Benchmarking Inference float precision type vgg19_bn
vgg19_bn model average inference time : 36.740665435791016ms
Benchmarking Inference float precision type mobilenet_v3_large
mobilenet_v3_large model average inference time : 22.719449996948242ms
Benchmarking Inference float precision type mobilenet_v3_small
mobilenet_v3_small model average inference time : 24.17999267578125ms
Benchmarking Inference float precision type shufflenet_v2_x0_5
shufflenet_v2_x0_5 model average inference time : 25.560874938964844ms
Benchmarking Inference float precision type shufflenet_v2_x1_0
shufflenet_v2_x1_0 model average inference time : 10.258355140686035ms
Benchmarking Inference float precision type shufflenet_v2_x1_5
shufflenet_v2_x1_5 model average inference time : 10.658831596374512ms
Benchmarking Inference float precision type shufflenet_v2_x2_0
shufflenet_v2_x2_0 model average inference time : 9.099092483520508ms
Benchmarking Training half precision type mnasnet0_5
mnasnet0_5 model average train time : 19.317455291748047ms
Benchmarking Training half precision type mnasnet0_75
mnasnet0_75 model average train time : 17.296757698059082ms
Benchmarking Training half precision type mnasnet1_0
mnasnet1_0 model average train time : 18.497648239135742ms
Benchmarking Training half precision type mnasnet1_3
mnasnet1_3 model average train time : 20.337824821472168ms
Benchmarking Training half precision type resnet18
resnet18 model average train time : 10.737395286560059ms
Benchmarking Training half precision type resnet34
resnet34 model average train time : 18.01840305328369ms
Benchmarking Training half precision type resnet50
resnet50 model average train time : 24.13780689239502ms
Benchmarking Training half precision type resnet101
resnet101 model average train time : 65.053391456604ms
Benchmarking Training half precision type resnet152
resnet152 model average train time : 87.77993202209473ms
Benchmarking Training half precision type resnext50_32x4d
resnext50_32x4d model average train time : 38.41778755187988ms
Benchmarking Training half precision type resnext101_32x8d
resnext101_32x8d model average train time : 78.39791297912598ms
Benchmarking Training half precision type resnext101_64x4d
resnext101_64x4d model average train time : 65.95728874206543ms
Benchmarking Training half precision type wide_resnet50_2
wide_resnet50_2 model average train time : 38.639774322509766ms
Benchmarking Training half precision type wide_resnet101_2
wide_resnet101_2 model average train time : 75.23865222930908ms
Benchmarking Training half precision type densenet121
densenet121 model average train time : 63.9487886428833ms
Benchmarking Training half precision type densenet161
densenet161 model average train time : 72.19789505004883ms
Benchmarking Training half precision type densenet169
densenet169 model average train time : 67.53844738006592ms
Benchmarking Training half precision type densenet201
densenet201 model average train time : 79.63869094848633ms
Benchmarking Training half precision type squeezenet1_0
squeezenet1_0 model average train time : 9.997620582580566ms
Benchmarking Training half precision type squeezenet1_1
squeezenet1_1 model average train time : 10.357756614685059ms
Benchmarking Training half precision type vgg11
vgg11 model average train time : 18.937969207763672ms
Benchmarking Training half precision type vgg11_bn
vgg11_bn model average train time : 21.8375301361084ms
Benchmarking Training half precision type vgg13
vgg13 model average train time : 26.059551239013672ms
Benchmarking Training half precision type vgg13_bn
vgg13_bn model average train time : 31.498489379882812ms
Benchmarking Training half precision type vgg16
vgg16 model average train time : 30.217652320861816ms
Benchmarking Training half precision type vgg16_bn
vgg16_bn model average train time : 36.59909725189209ms
Benchmarking Training half precision type vgg19
vgg19 model average train time : 33.51834774017334ms
Benchmarking Training half precision type vgg19_bn
vgg19_bn model average train time : 41.778907775878906ms
Benchmarking Training half precision type mobilenet_v3_large
mobilenet_v3_large model average train time : 26.117448806762695ms
Benchmarking Training half precision type mobilenet_v3_small
mobilenet_v3_small model average train time : 20.058345794677734ms
Benchmarking Training half precision type shufflenet_v2_x0_5
shufflenet_v2_x0_5 model average train time : 20.09774684906006ms
Benchmarking Training half precision type shufflenet_v2_x1_0
shufflenet_v2_x1_0 model average train time : 23.45808506011963ms
Benchmarking Training half precision type shufflenet_v2_x1_5
shufflenet_v2_x1_5 model average train time : 24.498424530029297ms
Benchmarking Training half precision type shufflenet_v2_x2_0
shufflenet_v2_x2_0 model average train time : 22.27804660797119ms
Benchmarking Inference half precision type mnasnet0_5
mnasnet0_5 model average inference time : 7.685980796813965ms
Benchmarking Inference half precision type mnasnet0_75
mnasnet0_75 model average inference time : 8.238997459411621ms
Benchmarking Inference half precision type mnasnet1_0
mnasnet1_0 model average inference time : 7.277264595031738ms
Benchmarking Inference half precision type mnasnet1_3
mnasnet1_3 model average inference time : 8.098416328430176ms
Benchmarking Inference half precision type resnet18
resnet18 model average inference time : 5.898685455322266ms
Benchmarking Inference half precision type resnet34
resnet34 model average inference time : 9.138631820678711ms
Benchmarking Inference half precision type resnet50
resnet50 model average inference time : 10.759201049804688ms
Benchmarking Inference half precision type resnet101
resnet101 model average inference time : 13.077878952026367ms
Benchmarking Inference half precision type resnet152
resnet152 model average inference time : 16.79670810699463ms
Benchmarking Inference half precision type resnext50_32x4d
resnext50_32x4d model average inference time : 9.577674865722656ms
Benchmarking Inference half precision type resnext101_32x8d
resnext101_32x8d model average inference time : 19.257173538208008ms
Benchmarking Inference half precision type resnext101_64x4d
resnext101_64x4d model average inference time : 19.3574857711792ms
Benchmarking Inference half precision type wide_resnet50_2
wide_resnet50_2 model average inference time : 10.996685028076172ms
Benchmarking Inference half precision type wide_resnet101_2
wide_resnet101_2 model average inference time : 18.69781494140625ms
Benchmarking Inference half precision type densenet121
densenet121 model average inference time : 15.078353881835938ms
Benchmarking Inference half precision type densenet161
densenet161 model average inference time : 20.338892936706543ms
Benchmarking Inference half precision type densenet169
densenet169 model average inference time : 22.104644775390625ms
Benchmarking Inference half precision type densenet201
densenet201 model average inference time : 24.018239974975586ms
Benchmarking Inference half precision type squeezenet1_0
squeezenet1_0 model average inference time : 4.998345375061035ms
Benchmarking Inference half precision type squeezenet1_1
squeezenet1_1 model average inference time : 4.978952407836914ms
Benchmarking Inference half precision type vgg11
vgg11 model average inference time : 12.93889045715332ms
Benchmarking Inference half precision type vgg11_bn
vgg11_bn model average inference time : 13.59870433807373ms
Benchmarking Inference half precision type vgg13
vgg13 model average inference time : 9.677634239196777ms
Benchmarking Inference half precision type vgg13_bn
vgg13_bn model average inference time : 11.417737007141113ms
Benchmarking Inference half precision type vgg16
vgg16 model average inference time : 10.67772388458252ms
Benchmarking Inference half precision type vgg16_bn
vgg16_bn model average inference time : 11.676826477050781ms
Benchmarking Inference half precision type vgg19
vgg19 model average inference time : 12.33820915222168ms
Benchmarking Inference half precision type vgg19_bn
vgg19_bn model average inference time : 13.658561706542969ms
Benchmarking Inference half precision type mobilenet_v3_large
mobilenet_v3_large model average inference time : 11.939544677734375ms
Benchmarking Inference half precision type mobilenet_v3_small
mobilenet_v3_small model average inference time : 12.039885520935059ms
Benchmarking Inference half precision type shufflenet_v2_x0_5
shufflenet_v2_x0_5 model average inference time : 10.122275352478027ms
Benchmarking Inference half precision type shufflenet_v2_x1_0
shufflenet_v2_x1_0 model average inference time : 10.29858112335205ms
Benchmarking Inference half precision type shufflenet_v2_x1_5
shufflenet_v2_x1_5 model average inference time : 8.597450256347656ms
Benchmarking Inference half precision type shufflenet_v2_x2_0
shufflenet_v2_x2_0 model average inference time : 14.09965991973877ms
Benchmarking Training double precision type mnasnet0_5
mnasnet0_5 model average train time : 29.278149604797363ms
Benchmarking Training double precision type mnasnet0_75
mnasnet0_75 model average train time : 32.03717231750488ms
Benchmarking Training double precision type mnasnet1_0
mnasnet1_0 model average train time : 35.578227043151855ms
Benchmarking Training double precision type mnasnet1_3
mnasnet1_3 model average train time : 42.65677452087402ms
Benchmarking Training double precision type resnet18
resnet18 model average train time : 42.737083435058594ms
Benchmarking Training double precision type resnet34
resnet34 model average train time : 76.87731266021729ms
Benchmarking Training double precision type resnet50
resnet50 model average train time : 97.04819202423096ms
Benchmarking Training double precision type resnet101
resnet101 model average train time : 172.39765167236328ms
Benchmarking Training double precision type resnet152
resnet152 model average train time : 247.73847579956055ms
Benchmarking Training double precision type resnext50_32x4d
resnext50_32x4d model average train time : 114.30838584899902ms
Benchmarking Training double precision type resnext101_32x8d
resnext101_32x8d model average train time : 352.13791370391846ms
Benchmarking Training double precision type resnext101_64x4d
resnext101_64x4d model average train time : 368.9488697052002ms
Benchmarking Training double precision type wide_resnet50_2
wide_resnet50_2 model average train time : 223.41819286346436ms
Benchmarking Training double precision type wide_resnet101_2
wide_resnet101_2 model average train time : 422.1772766113281ms
Benchmarking Training double precision type densenet121
densenet121 model average train time : 111.37905597686768ms
Benchmarking Training double precision type densenet161
densenet161 model average train time : 250.80832958221436ms
Benchmarking Training double precision type densenet169
densenet169 model average train time : 131.77853107452393ms
Benchmarking Training double precision type densenet201
densenet201 model average train time : 171.81907176971436ms
Benchmarking Training double precision type squeezenet1_0
squeezenet1_0 model average train time : 30.43745994567871ms
Benchmarking Training double precision type squeezenet1_1
squeezenet1_1 model average train time : 20.757312774658203ms
Benchmarking Training double precision type vgg11
vgg11 model average train time : 125.81704139709473ms
Benchmarking Training double precision type vgg11_bn
vgg11_bn model average train time : 136.49741649627686ms
Benchmarking Training double precision type vgg13
vgg13 model average train time : 191.9779872894287ms
Benchmarking Training double precision type vgg13_bn
vgg13_bn model average train time : 215.01746654510498ms
Benchmarking Training double precision type vgg16
vgg16 model average train time : 254.417085647583ms
Benchmarking Training double precision type vgg16_bn
vgg16_bn model average train time : 285.3776979446411ms
Benchmarking Training double precision type vgg19
vgg19 model average train time : 321.7171239852905ms
Benchmarking Training double precision type vgg19_bn
vgg19_bn model average train time : 364.0787363052368ms
Benchmarking Training double precision type mobilenet_v3_large
mobilenet_v3_large model average train time : 31.97683334350586ms
Benchmarking Training double precision type mobilenet_v3_small
mobilenet_v3_small model average train time : 20.437145233154297ms
Benchmarking Training double precision type shufflenet_v2_x0_5
shufflenet_v2_x0_5 model average train time : 21.977334022521973ms
Benchmarking Training double precision type shufflenet_v2_x1_0
shufflenet_v2_x1_0 model average train time : 23.27650547027588ms
Benchmarking Training double precision type shufflenet_v2_x1_5
shufflenet_v2_x1_5 model average train time : 28.07706356048584ms
Benchmarking Training double precision type shufflenet_v2_x2_0
shufflenet_v2_x2_0 model average train time : 30.9163761138916ms
Benchmarking Inference double precision type mnasnet0_5
mnasnet0_5 model average inference time : 9.1965913772583ms
Benchmarking Inference double precision type mnasnet0_75
mnasnet0_75 model average inference time : 10.13648509979248ms
Benchmarking Inference double precision type mnasnet1_0
mnasnet1_0 model average inference time : 11.316795349121094ms
Benchmarking Inference double precision type mnasnet1_3
mnasnet1_3 model average inference time : 13.03621768951416ms
Benchmarking Inference double precision type resnet18
resnet18 model average inference time : 15.237846374511719ms
Benchmarking Inference double precision type resnet34
resnet34 model average inference time : 24.8165225982666ms
Benchmarking Inference double precision type resnet50
resnet50 model average inference time : 30.696945190429688ms
Benchmarking Inference double precision type resnet101
resnet101 model average inference time : 51.01701736450195ms
Benchmarking Inference double precision type resnet152
resnet152 model average inference time : 72.44745254516602ms
Benchmarking Inference double precision type resnext50_32x4d
resnext50_32x4d model average inference time : 35.83712100982666ms
Benchmarking Inference double precision type resnext101_32x8d
resnext101_32x8d model average inference time : 99.85790729522705ms
Benchmarking Inference double precision type resnext101_64x4d
resnext101_64x4d model average inference time : 99.69750881195068ms
Benchmarking Inference double precision type wide_resnet50_2
wide_resnet50_2 model average inference time : 62.41707801818848ms
Benchmarking Inference double precision type wide_resnet101_2
wide_resnet101_2 model average inference time : 116.47632598876953ms
Benchmarking Inference double precision type densenet121
densenet121 model average inference time : 34.49748516082764ms
Benchmarking Inference double precision type densenet161
densenet161 model average inference time : 78.39729309082031ms
Benchmarking Inference double precision type densenet169
densenet169 model average inference time : 45.37738800048828ms
Benchmarking Inference double precision type densenet201
densenet201 model average inference time : 57.647833824157715ms
Benchmarking Inference double precision type squeezenet1_0
squeezenet1_0 model average inference time : 12.858242988586426ms
Benchmarking Inference double precision type squeezenet1_1
squeezenet1_1 model average inference time : 9.777970314025879ms
Benchmarking Inference double precision type vgg11
vgg11 model average inference time : 42.438035011291504ms
Benchmarking Inference double precision type vgg11_bn
vgg11_bn model average inference time : 44.33735370635986ms
Benchmarking Inference double precision type vgg13
vgg13 model average inference time : 61.6082763671875ms
Benchmarking Inference double precision type vgg13_bn
vgg13_bn model average inference time : 63.85744094848633ms
Benchmarking Inference double precision type vgg16
vgg16 model average inference time : 79.07710075378418ms
Benchmarking Inference double precision type vgg16_bn
vgg16_bn model average inference time : 82.25726127624512ms
Benchmarking Inference double precision type vgg19
vgg19 model average inference time : 97.41812705993652ms
Benchmarking Inference double precision type vgg19_bn
vgg19_bn model average inference time : 100.57655811309814ms
Benchmarking Inference double precision type mobilenet_v3_large
mobilenet_v3_large model average inference time : 10.818839073181152ms
Benchmarking Inference double precision type mobilenet_v3_small
mobilenet_v3_small model average inference time : 8.477234840393066ms
Benchmarking Inference double precision type shufflenet_v2_x0_5
shufflenet_v2_x0_5 model average inference time : 9.297652244567871ms
Benchmarking Inference double precision type shufflenet_v2_x1_0
shufflenet_v2_x1_0 model average inference time : 10.63753604888916ms
Benchmarking Inference double precision type shufflenet_v2_x1_5
shufflenet_v2_x1_5 model average inference time : 11.77729606628418ms
Benchmarking Inference double precision type shufflenet_v2_x2_0
shufflenet_v2_x2_0 model average inference time : 12.73733139038086ms
benchmark end : 2024/09/24 17:00:47
Process finished with exit code 0
result.zip
C:\Users\levi\AppData\Local\Microsoft\WindowsApps\python3.12.exe C:\Users\levi\Desktop\总目录-兆衍\跑分\pytorch-gpu-benchmark-main\benchmark_models.py
benchmark start : 2024/09/24 16:07:45
Number of GPUs on current device : 1
CUDA Version : 11.8
Cudnn Version : 90100
Device Name : Tesla V100-SXM2-32GB
uname_result(system='Windows', node='DESKTOP-V8VCGPS', release='10', version='10.0.19045', machine='AMD64')
scpufreq(current=2300.0, min=0.0, max=2300.0)
cpu_count: 8
memory_available: 1331605504
Benchmarking Training float precision type mnasnet0_5
mnasnet0_5 model average train time : 40.779852867126465ms
Benchmarking Training float precision type mnasnet0_75
mnasnet0_75 model average train time : 57.56028652191162ms
Benchmarking Training float precision type mnasnet1_0
mnasnet1_0 model average train time : 62.10038185119629ms
Benchmarking Training float precision type mnasnet1_3
mnasnet1_3 model average train time : 60.16016960144043ms
Benchmarking Training float precision type resnet18
resnet18 model average train time : 32.97965049743652ms
Benchmarking Training float precision type resnet34
resnet34 model average train time : 49.080657958984375ms
Benchmarking Training float precision type resnet50
resnet50 model average train time : 58.658742904663086ms
Benchmarking Training float precision type resnet101
resnet101 model average train time : 95.45299053192139ms
Benchmarking Training float precision type resnet152
resnet152 model average train time : 118.63776206970215ms
Benchmarking Training float precision type resnext50_32x4d
resnext50_32x4d model average train time : 94.1808557510376ms
Benchmarking Training float precision type resnext101_32x8d
resnext101_32x8d model average train time : 247.60605335235596ms
Benchmarking Training float precision type resnext101_64x4d
resnext101_64x4d model average train time : 263.53647232055664ms
Benchmarking Training float precision type wide_resnet50_2
wide_resnet50_2 model average train time : 102.8178882598877ms
Benchmarking Training float precision type wide_resnet101_2
wide_resnet101_2 model average train time : 169.45948123931885ms
Benchmarking Training float precision type densenet121
densenet121 model average train time : 70.9781551361084ms
Benchmarking Training float precision type densenet161
densenet161 model average train time : 138.28054904937744ms
Benchmarking Training float precision type densenet169
densenet169 model average train time : 94.05195236206055ms
Benchmarking Training float precision type densenet201
densenet201 model average train time : 118.00044059753418ms
Benchmarking Training float precision type squeezenet1_0
squeezenet1_0 model average train time : 24.398889541625977ms
Benchmarking Training float precision type squeezenet1_1
squeezenet1_1 model average train time : 23.419828414916992ms
Benchmarking Training float precision type vgg11
vgg11 model average train time : 52.15620517730713ms
Benchmarking Training float precision type vgg11_bn
vgg11_bn model average train time : 57.5331974029541ms
Benchmarking Training float precision type vgg13
vgg13 model average train time : 64.07845973968506ms
Benchmarking Training float precision type vgg13_bn
vgg13_bn model average train time : 75.23913860321045ms
Benchmarking Training float precision type vgg16
vgg16 model average train time : 79.0393877029419ms
Benchmarking Training float precision type vgg16_bn
vgg16_bn model average train time : 84.83956813812256ms
Benchmarking Training float precision type vgg19
vgg19 model average train time : 72.85277843475342ms
Benchmarking Training float precision type vgg19_bn
vgg19_bn model average train time : 81.79934024810791ms
Benchmarking Training float precision type mobilenet_v3_large
mobilenet_v3_large model average train time : 21.939353942871094ms
Benchmarking Training float precision type mobilenet_v3_small
mobilenet_v3_small model average train time : 15.919809341430664ms
Benchmarking Training float precision type shufflenet_v2_x0_5
shufflenet_v2_x0_5 model average train time : 19.15968894958496ms
Benchmarking Training float precision type shufflenet_v2_x1_0
shufflenet_v2_x1_0 model average train time : 21.83973789215088ms
Benchmarking Training float precision type shufflenet_v2_x1_5
shufflenet_v2_x1_5 model average train time : 19.1195011138916ms
Benchmarking Training float precision type shufflenet_v2_x2_0
shufflenet_v2_x2_0 model average train time : 22.25897789001465ms
Benchmarking Inference float precision type mnasnet0_5
mnasnet0_5 model average inference time : 8.699288368225098ms
Benchmarking Inference float precision type mnasnet0_75
mnasnet0_75 model average inference time : 9.519319534301758ms
Benchmarking Inference float precision type mnasnet1_0
mnasnet1_0 model average inference time : 18.99911403656006ms
Benchmarking Inference float precision type mnasnet1_3
mnasnet1_3 model average inference time : 22.16010093688965ms
Benchmarking Inference float precision type resnet18
resnet18 model average inference time : 22.259244918823242ms
Benchmarking Inference float precision type resnet34
resnet34 model average inference time : 21.059303283691406ms
Benchmarking Inference float precision type resnet50
resnet50 model average inference time : 27.0988130569458ms
Benchmarking Inference float precision type resnet101
resnet101 model average inference time : 36.17978096008301ms
Benchmarking Inference float precision type resnet152
resnet152 model average inference time : 45.93863487243652ms
Benchmarking Inference float precision type resnext50_32x4d
resnext50_32x4d model average inference time : 31.87857151031494ms
Benchmarking Inference float precision type resnext101_32x8d
resnext101_32x8d model average inference time : 76.87961101531982ms
Benchmarking Inference float precision type resnext101_64x4d
resnext101_64x4d model average inference time : 79.81907844543457ms
Benchmarking Inference float precision type wide_resnet50_2
wide_resnet50_2 model average inference time : 36.97860240936279ms
Benchmarking Inference float precision type wide_resnet101_2
wide_resnet101_2 model average inference time : 59.099273681640625ms
Benchmarking Inference float precision type densenet121
densenet121 model average inference time : 31.83908462524414ms
Benchmarking Inference float precision type densenet161
densenet161 model average inference time : 50.73944568634033ms
Benchmarking Inference float precision type densenet169
densenet169 model average inference time : 39.839816093444824ms
Benchmarking Inference float precision type densenet201
densenet201 model average inference time : 34.97932434082031ms
Benchmarking Inference float precision type squeezenet1_0
squeezenet1_0 model average inference time : 13.399724960327148ms
Benchmarking Inference float precision type squeezenet1_1
squeezenet1_1 model average inference time : 14.160284996032715ms
Benchmarking Inference float precision type vgg11
vgg11 model average inference time : 17.058839797973633ms
Benchmarking Inference float precision type vgg11_bn
vgg11_bn model average inference time : 27.99907684326172ms
Benchmarking Inference float precision type vgg13
vgg13 model average inference time : 23.240089416503906ms
Benchmarking Inference float precision type vgg13_bn
vgg13_bn model average inference time : 27.69937038421631ms
Benchmarking Inference float precision type vgg16
vgg16 model average inference time : 24.059300422668457ms
Benchmarking Inference float precision type vgg16_bn
vgg16_bn model average inference time : 37.35865116119385ms
Benchmarking Inference float precision type vgg19
vgg19 model average inference time : 28.23927879333496ms
Benchmarking Inference float precision type vgg19_bn
vgg19_bn model average inference time : 36.740665435791016ms
Benchmarking Inference float precision type mobilenet_v3_large
mobilenet_v3_large model average inference time : 22.719449996948242ms
Benchmarking Inference float precision type mobilenet_v3_small
mobilenet_v3_small model average inference time : 24.17999267578125ms
Benchmarking Inference float precision type shufflenet_v2_x0_5
shufflenet_v2_x0_5 model average inference time : 25.560874938964844ms
Benchmarking Inference float precision type shufflenet_v2_x1_0
shufflenet_v2_x1_0 model average inference time : 10.258355140686035ms
Benchmarking Inference float precision type shufflenet_v2_x1_5
shufflenet_v2_x1_5 model average inference time : 10.658831596374512ms
Benchmarking Inference float precision type shufflenet_v2_x2_0
shufflenet_v2_x2_0 model average inference time : 9.099092483520508ms
Benchmarking Training half precision type mnasnet0_5
mnasnet0_5 model average train time : 19.317455291748047ms
Benchmarking Training half precision type mnasnet0_75
mnasnet0_75 model average train time : 17.296757698059082ms
Benchmarking Training half precision type mnasnet1_0
mnasnet1_0 model average train time : 18.497648239135742ms
Benchmarking Training half precision type mnasnet1_3
mnasnet1_3 model average train time : 20.337824821472168ms
Benchmarking Training half precision type resnet18
resnet18 model average train time : 10.737395286560059ms
Benchmarking Training half precision type resnet34
resnet34 model average train time : 18.01840305328369ms
Benchmarking Training half precision type resnet50
resnet50 model average train time : 24.13780689239502ms
Benchmarking Training half precision type resnet101
resnet101 model average train time : 65.053391456604ms
Benchmarking Training half precision type resnet152
resnet152 model average train time : 87.77993202209473ms
Benchmarking Training half precision type resnext50_32x4d
resnext50_32x4d model average train time : 38.41778755187988ms
Benchmarking Training half precision type resnext101_32x8d
resnext101_32x8d model average train time : 78.39791297912598ms
Benchmarking Training half precision type resnext101_64x4d
resnext101_64x4d model average train time : 65.95728874206543ms
Benchmarking Training half precision type wide_resnet50_2
wide_resnet50_2 model average train time : 38.639774322509766ms
Benchmarking Training half precision type wide_resnet101_2
wide_resnet101_2 model average train time : 75.23865222930908ms
Benchmarking Training half precision type densenet121
densenet121 model average train time : 63.9487886428833ms
Benchmarking Training half precision type densenet161
densenet161 model average train time : 72.19789505004883ms
Benchmarking Training half precision type densenet169
densenet169 model average train time : 67.53844738006592ms
Benchmarking Training half precision type densenet201
densenet201 model average train time : 79.63869094848633ms
Benchmarking Training half precision type squeezenet1_0
squeezenet1_0 model average train time : 9.997620582580566ms
Benchmarking Training half precision type squeezenet1_1
squeezenet1_1 model average train time : 10.357756614685059ms
Benchmarking Training half precision type vgg11
vgg11 model average train time : 18.937969207763672ms
Benchmarking Training half precision type vgg11_bn
vgg11_bn model average train time : 21.8375301361084ms
Benchmarking Training half precision type vgg13
vgg13 model average train time : 26.059551239013672ms
Benchmarking Training half precision type vgg13_bn
vgg13_bn model average train time : 31.498489379882812ms
Benchmarking Training half precision type vgg16
vgg16 model average train time : 30.217652320861816ms
Benchmarking Training half precision type vgg16_bn
vgg16_bn model average train time : 36.59909725189209ms
Benchmarking Training half precision type vgg19
vgg19 model average train time : 33.51834774017334ms
Benchmarking Training half precision type vgg19_bn
vgg19_bn model average train time : 41.778907775878906ms
Benchmarking Training half precision type mobilenet_v3_large
mobilenet_v3_large model average train time : 26.117448806762695ms
Benchmarking Training half precision type mobilenet_v3_small
mobilenet_v3_small model average train time : 20.058345794677734ms
Benchmarking Training half precision type shufflenet_v2_x0_5
shufflenet_v2_x0_5 model average train time : 20.09774684906006ms
Benchmarking Training half precision type shufflenet_v2_x1_0
shufflenet_v2_x1_0 model average train time : 23.45808506011963ms
Benchmarking Training half precision type shufflenet_v2_x1_5
shufflenet_v2_x1_5 model average train time : 24.498424530029297ms
Benchmarking Training half precision type shufflenet_v2_x2_0
shufflenet_v2_x2_0 model average train time : 22.27804660797119ms
Benchmarking Inference half precision type mnasnet0_5
mnasnet0_5 model average inference time : 7.685980796813965ms
Benchmarking Inference half precision type mnasnet0_75
mnasnet0_75 model average inference time : 8.238997459411621ms
Benchmarking Inference half precision type mnasnet1_0
mnasnet1_0 model average inference time : 7.277264595031738ms
Benchmarking Inference half precision type mnasnet1_3
mnasnet1_3 model average inference time : 8.098416328430176ms
Benchmarking Inference half precision type resnet18
resnet18 model average inference time : 5.898685455322266ms
Benchmarking Inference half precision type resnet34
resnet34 model average inference time : 9.138631820678711ms
Benchmarking Inference half precision type resnet50
resnet50 model average inference time : 10.759201049804688ms
Benchmarking Inference half precision type resnet101
resnet101 model average inference time : 13.077878952026367ms
Benchmarking Inference half precision type resnet152
resnet152 model average inference time : 16.79670810699463ms
Benchmarking Inference half precision type resnext50_32x4d
resnext50_32x4d model average inference time : 9.577674865722656ms
Benchmarking Inference half precision type resnext101_32x8d
resnext101_32x8d model average inference time : 19.257173538208008ms
Benchmarking Inference half precision type resnext101_64x4d
resnext101_64x4d model average inference time : 19.3574857711792ms
Benchmarking Inference half precision type wide_resnet50_2
wide_resnet50_2 model average inference time : 10.996685028076172ms
Benchmarking Inference half precision type wide_resnet101_2
wide_resnet101_2 model average inference time : 18.69781494140625ms
Benchmarking Inference half precision type densenet121
densenet121 model average inference time : 15.078353881835938ms
Benchmarking Inference half precision type densenet161
densenet161 model average inference time : 20.338892936706543ms
Benchmarking Inference half precision type densenet169
densenet169 model average inference time : 22.104644775390625ms
Benchmarking Inference half precision type densenet201
densenet201 model average inference time : 24.018239974975586ms
Benchmarking Inference half precision type squeezenet1_0
squeezenet1_0 model average inference time : 4.998345375061035ms
Benchmarking Inference half precision type squeezenet1_1
squeezenet1_1 model average inference time : 4.978952407836914ms
Benchmarking Inference half precision type vgg11
vgg11 model average inference time : 12.93889045715332ms
Benchmarking Inference half precision type vgg11_bn
vgg11_bn model average inference time : 13.59870433807373ms
Benchmarking Inference half precision type vgg13
vgg13 model average inference time : 9.677634239196777ms
Benchmarking Inference half precision type vgg13_bn
vgg13_bn model average inference time : 11.417737007141113ms
Benchmarking Inference half precision type vgg16
vgg16 model average inference time : 10.67772388458252ms
Benchmarking Inference half precision type vgg16_bn
vgg16_bn model average inference time : 11.676826477050781ms
Benchmarking Inference half precision type vgg19
vgg19 model average inference time : 12.33820915222168ms
Benchmarking Inference half precision type vgg19_bn
vgg19_bn model average inference time : 13.658561706542969ms
Benchmarking Inference half precision type mobilenet_v3_large
mobilenet_v3_large model average inference time : 11.939544677734375ms
Benchmarking Inference half precision type mobilenet_v3_small
mobilenet_v3_small model average inference time : 12.039885520935059ms
Benchmarking Inference half precision type shufflenet_v2_x0_5
shufflenet_v2_x0_5 model average inference time : 10.122275352478027ms
Benchmarking Inference half precision type shufflenet_v2_x1_0
shufflenet_v2_x1_0 model average inference time : 10.29858112335205ms
Benchmarking Inference half precision type shufflenet_v2_x1_5
shufflenet_v2_x1_5 model average inference time : 8.597450256347656ms
Benchmarking Inference half precision type shufflenet_v2_x2_0
shufflenet_v2_x2_0 model average inference time : 14.09965991973877ms
Benchmarking Training double precision type mnasnet0_5
mnasnet0_5 model average train time : 29.278149604797363ms
Benchmarking Training double precision type mnasnet0_75
mnasnet0_75 model average train time : 32.03717231750488ms
Benchmarking Training double precision type mnasnet1_0
mnasnet1_0 model average train time : 35.578227043151855ms
Benchmarking Training double precision type mnasnet1_3
mnasnet1_3 model average train time : 42.65677452087402ms
Benchmarking Training double precision type resnet18
resnet18 model average train time : 42.737083435058594ms
Benchmarking Training double precision type resnet34
resnet34 model average train time : 76.87731266021729ms
Benchmarking Training double precision type resnet50
resnet50 model average train time : 97.04819202423096ms
Benchmarking Training double precision type resnet101
resnet101 model average train time : 172.39765167236328ms
Benchmarking Training double precision type resnet152
resnet152 model average train time : 247.73847579956055ms
Benchmarking Training double precision type resnext50_32x4d
resnext50_32x4d model average train time : 114.30838584899902ms
Benchmarking Training double precision type resnext101_32x8d
resnext101_32x8d model average train time : 352.13791370391846ms
Benchmarking Training double precision type resnext101_64x4d
resnext101_64x4d model average train time : 368.9488697052002ms
Benchmarking Training double precision type wide_resnet50_2
wide_resnet50_2 model average train time : 223.41819286346436ms
Benchmarking Training double precision type wide_resnet101_2
wide_resnet101_2 model average train time : 422.1772766113281ms
Benchmarking Training double precision type densenet121
densenet121 model average train time : 111.37905597686768ms
Benchmarking Training double precision type densenet161
densenet161 model average train time : 250.80832958221436ms
Benchmarking Training double precision type densenet169
densenet169 model average train time : 131.77853107452393ms
Benchmarking Training double precision type densenet201
densenet201 model average train time : 171.81907176971436ms
Benchmarking Training double precision type squeezenet1_0
squeezenet1_0 model average train time : 30.43745994567871ms
Benchmarking Training double precision type squeezenet1_1
squeezenet1_1 model average train time : 20.757312774658203ms
Benchmarking Training double precision type vgg11
vgg11 model average train time : 125.81704139709473ms
Benchmarking Training double precision type vgg11_bn
vgg11_bn model average train time : 136.49741649627686ms
Benchmarking Training double precision type vgg13
vgg13 model average train time : 191.9779872894287ms
Benchmarking Training double precision type vgg13_bn
vgg13_bn model average train time : 215.01746654510498ms
Benchmarking Training double precision type vgg16
vgg16 model average train time : 254.417085647583ms
Benchmarking Training double precision type vgg16_bn
vgg16_bn model average train time : 285.3776979446411ms
Benchmarking Training double precision type vgg19
vgg19 model average train time : 321.7171239852905ms
Benchmarking Training double precision type vgg19_bn
vgg19_bn model average train time : 364.0787363052368ms
Benchmarking Training double precision type mobilenet_v3_large
mobilenet_v3_large model average train time : 31.97683334350586ms
Benchmarking Training double precision type mobilenet_v3_small
mobilenet_v3_small model average train time : 20.437145233154297ms
Benchmarking Training double precision type shufflenet_v2_x0_5
shufflenet_v2_x0_5 model average train time : 21.977334022521973ms
Benchmarking Training double precision type shufflenet_v2_x1_0
shufflenet_v2_x1_0 model average train time : 23.27650547027588ms
Benchmarking Training double precision type shufflenet_v2_x1_5
shufflenet_v2_x1_5 model average train time : 28.07706356048584ms
Benchmarking Training double precision type shufflenet_v2_x2_0
shufflenet_v2_x2_0 model average train time : 30.9163761138916ms
Benchmarking Inference double precision type mnasnet0_5
mnasnet0_5 model average inference time : 9.1965913772583ms
Benchmarking Inference double precision type mnasnet0_75
mnasnet0_75 model average inference time : 10.13648509979248ms
Benchmarking Inference double precision type mnasnet1_0
mnasnet1_0 model average inference time : 11.316795349121094ms
Benchmarking Inference double precision type mnasnet1_3
mnasnet1_3 model average inference time : 13.03621768951416ms
Benchmarking Inference double precision type resnet18
resnet18 model average inference time : 15.237846374511719ms
Benchmarking Inference double precision type resnet34
resnet34 model average inference time : 24.8165225982666ms
Benchmarking Inference double precision type resnet50
resnet50 model average inference time : 30.696945190429688ms
Benchmarking Inference double precision type resnet101
resnet101 model average inference time : 51.01701736450195ms
Benchmarking Inference double precision type resnet152
resnet152 model average inference time : 72.44745254516602ms
Benchmarking Inference double precision type resnext50_32x4d
resnext50_32x4d model average inference time : 35.83712100982666ms
Benchmarking Inference double precision type resnext101_32x8d
resnext101_32x8d model average inference time : 99.85790729522705ms
Benchmarking Inference double precision type resnext101_64x4d
resnext101_64x4d model average inference time : 99.69750881195068ms
Benchmarking Inference double precision type wide_resnet50_2
wide_resnet50_2 model average inference time : 62.41707801818848ms
Benchmarking Inference double precision type wide_resnet101_2
wide_resnet101_2 model average inference time : 116.47632598876953ms
Benchmarking Inference double precision type densenet121
densenet121 model average inference time : 34.49748516082764ms
Benchmarking Inference double precision type densenet161
densenet161 model average inference time : 78.39729309082031ms
Benchmarking Inference double precision type densenet169
densenet169 model average inference time : 45.37738800048828ms
Benchmarking Inference double precision type densenet201
densenet201 model average inference time : 57.647833824157715ms
Benchmarking Inference double precision type squeezenet1_0
squeezenet1_0 model average inference time : 12.858242988586426ms
Benchmarking Inference double precision type squeezenet1_1
squeezenet1_1 model average inference time : 9.777970314025879ms
Benchmarking Inference double precision type vgg11
vgg11 model average inference time : 42.438035011291504ms
Benchmarking Inference double precision type vgg11_bn
vgg11_bn model average inference time : 44.33735370635986ms
Benchmarking Inference double precision type vgg13
vgg13 model average inference time : 61.6082763671875ms
Benchmarking Inference double precision type vgg13_bn
vgg13_bn model average inference time : 63.85744094848633ms
Benchmarking Inference double precision type vgg16
vgg16 model average inference time : 79.07710075378418ms
Benchmarking Inference double precision type vgg16_bn
vgg16_bn model average inference time : 82.25726127624512ms
Benchmarking Inference double precision type vgg19
vgg19 model average inference time : 97.41812705993652ms
Benchmarking Inference double precision type vgg19_bn
vgg19_bn model average inference time : 100.57655811309814ms
Benchmarking Inference double precision type mobilenet_v3_large
mobilenet_v3_large model average inference time : 10.818839073181152ms
Benchmarking Inference double precision type mobilenet_v3_small
mobilenet_v3_small model average inference time : 8.477234840393066ms
Benchmarking Inference double precision type shufflenet_v2_x0_5
shufflenet_v2_x0_5 model average inference time : 9.297652244567871ms
Benchmarking Inference double precision type shufflenet_v2_x1_0
shufflenet_v2_x1_0 model average inference time : 10.63753604888916ms
Benchmarking Inference double precision type shufflenet_v2_x1_5
shufflenet_v2_x1_5 model average inference time : 11.77729606628418ms
Benchmarking Inference double precision type shufflenet_v2_x2_0
shufflenet_v2_x2_0 model average inference time : 12.73733139038086ms
benchmark end : 2024/09/24 17:00:47
Process finished with exit code 0
result.zip