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tb-output-eval-full.json
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tb-output-eval-full.json
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[
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"optimal_tflops_bs": 256
}
},
{
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"device": "cuda",
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"--precision",
"fp32"
],
"status": "ValueError",
"batch_size": null,
"precision": "fp32",
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}
},
{
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],
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"tflops": 16.19696335505791
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{
"batch_size": 2097152,
"latency_ms": 0.00014883264493942261,
"tflops": 16.21412363829371
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{
"batch_size": 4194304,
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"tflops": 16.224365583789453
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{
"batch_size": 8388608,
"latency_ms": 3.720184600353241e-05,
"tflops": 16.215509248189253
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{
"batch_size": 16777216,
"latency_ms": 1.8601801782846452e-05,
"tflops": 16.20808158823213
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{
"batch_size": 33554432,
"latency_ms": 9.304227843880653e-06,
"tflops": 16.210741470953828
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{
"batch_size": 67108864,
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{
"batch_size": 134217728,
"latency_ms": 2.3270045816898346e-06,
"tflops": 16.193582786408115
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{
"batch_size": 268435456,
"latency_ms": 1.162682332098484e-06,
"tflops": 16.222817391623494
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{
"batch_size": 536870912,
"latency_ms": 5.815115217119456e-07,
"tflops": 16.204510844003334
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],
"optimal_tflops_bs": 16384
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},
{
"name": "dcgan",
"test": "eval",
"device": "cuda",
"extra_args": [
"--precision",
"fp32"
],
"status": "RuntimeError",
"batch_size": null,
"precision": "fp32",
"results": {
"details": [
{
"batch_size": 1,
"latency_ms": 1.270156,
"tflops": 0.0
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{
"batch_size": 2,
"latency_ms": 0.643582,
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{
"batch_size": 4,
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{
"batch_size": 8,
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{
"batch_size": 16,
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{
"batch_size": 32,
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{
"batch_size": 64,
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{
"batch_size": 128,
"latency_ms": 0.038447734375,
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{
"batch_size": 256,
"latency_ms": 0.034684013671875,
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{
"batch_size": 512,
"latency_ms": 0.031615443359375,
"tflops": 13.468588901786598
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{
"batch_size": 1024,
"latency_ms": 0.030425892578125002,
"tflops": 14.36160572460046
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{
"batch_size": 2048,
"latency_ms": 0.029643124023437502,
"tflops": 14.94490215393634
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{
"batch_size": 4096,
"latency_ms": 0.029329715698242186,
"tflops": 15.046173957100772
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{
"batch_size": 8192,
"latency_ms": 0.029059907775878906,
"tflops": 15.209988253725006
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{
"batch_size": 16384,
"latency_ms": 0.02426745083618164,
"tflops": 15.250146260654127
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{
"batch_size": 32768,
"latency_ms": 0.023357963150024412,
"tflops": 15.598511305649842
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],
"error_message": "CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 39.59 GiB total capacity; 27.02 GiB already allocated; 7.11 GiB free; 30.04 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
"optimal_tflops_bs": 32768
}
},
{
"name": "demucs",
"test": "eval",
"device": "cuda",
"extra_args": [
"--precision",
"fp32"
],
"status": "RuntimeError",
"batch_size": null,
"precision": "fp32",
"results": {
"details": [
{
"batch_size": 1,
"latency_ms": 30.9735275,
"tflops": 4.323714704879502
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{
"batch_size": 2,
"latency_ms": 19.24750325,
"tflops": 6.895040003222739
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{
"batch_size": 4,
"latency_ms": 14.731321125000001,
"tflops": 9.426120775946746
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{
"batch_size": 8,
"latency_ms": 11.80227575,
"tflops": 11.598318446189046
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{
"batch_size": 16,
"latency_ms": 11.672525687499999,
"tflops": 12.822537181721891
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{
"batch_size": 32,
"latency_ms": 9.651182546874999,
"tflops": 13.915635250071396
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{
"batch_size": 64,
"latency_ms": 8.1538642265625,
"tflops": 14.88382546886645
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],
"error_message": "CUDA out of memory. Tried to allocate 2.83 GiB (GPU 0; 39.59 GiB total capacity; 35.95 GiB already allocated; 529.94 MiB free; 36.59 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
"optimal_tflops_bs": 64
}
},
{
"name": "densenet121",
"test": "eval",
"device": "cuda",
"extra_args": [
"--precision",
"fp32"
],
"status": "RuntimeError",
"batch_size": null,
"precision": "fp32",
"results": {
"details": [
{
"batch_size": 1,
"latency_ms": 24.451738499999998,
"tflops": 0.2556806894555885
},
{
"batch_size": 2,
"latency_ms": 12.1844945,
"tflops": 0.5042176617771347
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{
"batch_size": 4,
"latency_ms": 6.10821375,
"tflops": 0.9649652195338684
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{
"batch_size": 8,
"latency_ms": 3.1573853124999998,
"tflops": 1.9119070716619155
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{
"batch_size": 16,
"latency_ms": 1.70972415625,
"tflops": 3.5722808859664403
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{
"batch_size": 32,
"latency_ms": 0.8436155937500001,
"tflops": 6.6807131508899875
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{
"batch_size": 64,
"latency_ms": 0.755049296875,
"tflops": 8.558701847333811
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{
"batch_size": 128,
"latency_ms": 0.70996569921875,
"tflops": 9.18386457052571
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{
"batch_size": 256,
"latency_ms": 0.67638378125,
"tflops": 9.642536210922819
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],
"error_message": "CUDA out of memory. Tried to allocate 196.00 MiB (GPU 0; 39.59 GiB total capacity; 36.45 GiB already allocated; 27.94 MiB free; 37.14 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
"optimal_tflops_bs": 256
}
},
{
"name": "detectron2_fasterrcnn_r_101_c4",
"test": "eval",
"device": "cuda",
"extra_args": [
"--precision",
"fp32"
],
"status": "RuntimeError",
"batch_size": null,
"precision": "fp32",
"results": {
"details": [
{
"batch_size": 1,
"latency_ms": 134.737031,
"tflops": 12.381518504645742
},
{
"batch_size": 2,
"latency_ms": 130.885589,
"tflops": 12.971628061857388
},
{
"batch_size": 4,
"latency_ms": 118.656409875,
"tflops": 13.314290808671261
},
{
"batch_size": 8,
"latency_ms": 118.987906625,
"tflops": 13.361375459470263
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],
"error_message": "CUDA out of memory. Tried to allocate 5.00 GiB (GPU 0; 39.59 GiB total capacity; 23.11 GiB already allocated; 3.21 GiB free; 33.95 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
"optimal_tflops_bs": 8
}
},
{
"name": "detectron2_fasterrcnn_r_101_dc5",
"test": "eval",
"device": "cuda",
"extra_args": [
"--precision",
"fp32"
],
"status": "RuntimeError",
"batch_size": null,
"precision": "fp32",
"results": {
"details": [
{
"batch_size": 1,
"latency_ms": 79.101825,
"tflops": 11.77746011003922
},
{
"batch_size": 2,
"latency_ms": 76.98907,
"tflops": 12.137367287976351
},
{
"batch_size": 4,
"latency_ms": 83.95261975,
"tflops": 13.62690708927239
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{
"batch_size": 8,
"latency_ms": 83.049676,
"tflops": 13.894351658019753
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{
"batch_size": 16,
"latency_ms": 89.88828321875,
"tflops": 14.217158268971623
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{
"batch_size": 32,
"latency_ms": 71.66626446875,
"tflops": 14.280330277403525
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],
"error_message": "CUDA out of memory. Tried to allocate 6.81 GiB (GPU 0; 39.59 GiB total capacity; 19.97 GiB already allocated; 4.36 GiB free; 32.81 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
"optimal_tflops_bs": 32
}
},
{
"name": "detectron2_fasterrcnn_r_101_fpn",
"test": "eval",
"device": "cuda",
"extra_args": [
"--precision",
"fp32"
],
"status": "RuntimeError",
"batch_size": null,
"precision": "fp32",
"results": {
"details": [
{
"batch_size": 1,
"latency_ms": 51.5630605,
"tflops": 9.689082237229501
},
{
"batch_size": 2,
"latency_ms": 45.96151425,
"tflops": 10.819751694845097
},
{
"batch_size": 4,
"latency_ms": 66.84438962499999,
"tflops": 12.717973729568138
},
{
"batch_size": 8,
"latency_ms": 56.46501775,
"tflops": 12.575254704611659
},
{
"batch_size": 16,
"latency_ms": 55.26244096875,
"tflops": 12.613603168551204
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{
"batch_size": 32,
"latency_ms": 53.853478,
"tflops": 12.511348164367423
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],
"error_message": "CUDA out of memory. Tried to allocate 6.89 GiB (GPU 0; 39.59 GiB total capacity; 22.86 GiB already allocated; 6.09 GiB free; 31.07 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
"optimal_tflops_bs": 4
}
},
{
"name": "detectron2_fasterrcnn_r_50_c4",
"test": "eval",
"device": "cuda",
"extra_args": [
"--precision",
"fp32"
],
"status": "RuntimeError",
"batch_size": null,
"precision": "fp32",
"results": {
"details": [
{
"batch_size": 1,
"latency_ms": 122.135843,
"tflops": 12.651333337222056
},
{
"batch_size": 2,
"latency_ms": 119.6167055,
"tflops": 13.020134208318028
},
{
"batch_size": 4,
"latency_ms": 103.505941,
"tflops": 13.206247860949457
},
{
"batch_size": 8,
"latency_ms": 95.5551531875,
"tflops": 13.23242824267645
},
{
"batch_size": 16,
"latency_ms": 87.24982928125,
"tflops": 13.16338033268922
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],
"error_message": "CUDA out of memory. Tried to allocate 8.70 GiB (GPU 0; 39.59 GiB total capacity; 30.74 GiB already allocated; 3.39 GiB free; 33.78 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
"optimal_tflops_bs": 8
}
},
{
"name": "detectron2_fasterrcnn_r_50_dc5",
"test": "eval",
"device": "cuda",
"extra_args": [
"--precision",
"fp32"
],
"status": "RuntimeError",
"batch_size": null,
"precision": "fp32",
"results": {
"details": [
{
"batch_size": 1,
"latency_ms": 65.69673,
"tflops": 11.812651592074232
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{
"batch_size": 2,
"latency_ms": 65.5241495,
"tflops": 12.099828540816912
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{