282 lines
12 KiB
Plaintext
282 lines
12 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Mode=test: NumWorkers= 1 BatchSize= 1 Time=33.559s Imgs/s= 38.14\n",
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"Mode=test: NumWorkers= 1 BatchSize= 2 Time=16.639s Imgs/s= 76.93\n",
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"Mode=test: NumWorkers= 1 BatchSize= 4 Time= 8.817s Imgs/s=145.17\n",
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"Mode=test: NumWorkers= 1 BatchSize= 8 Time= 8.802s Imgs/s=145.41\n",
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"Mode=test: NumWorkers= 1 BatchSize=16 Time= 9.094s Imgs/s=140.76\n",
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"Mode=test: NumWorkers= 1 BatchSize=32 Time= 8.247s Imgs/s=155.21\n",
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"Mode=test: NumWorkers= 2 BatchSize= 1 Time=34.151s Imgs/s= 37.48\n",
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"Mode=test: NumWorkers= 2 BatchSize= 2 Time=16.366s Imgs/s= 78.21\n",
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"Mode=test: NumWorkers= 2 BatchSize= 4 Time= 7.701s Imgs/s=166.20\n",
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"Mode=test: NumWorkers= 2 BatchSize= 8 Time= 3.888s Imgs/s=329.25\n",
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"Mode=test: NumWorkers= 2 BatchSize=16 Time= 3.824s Imgs/s=334.75\n",
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"Mode=test: NumWorkers= 2 BatchSize=32 Time= 3.706s Imgs/s=345.38\n",
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"Mode=test: NumWorkers= 4 BatchSize= 1 Time=34.202s Imgs/s= 37.43\n",
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"Mode=test: NumWorkers= 4 BatchSize= 2 Time=16.350s Imgs/s= 78.29\n",
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"Mode=test: NumWorkers= 4 BatchSize= 4 Time= 7.816s Imgs/s=163.76\n",
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"Mode=test: NumWorkers= 4 BatchSize= 8 Time= 3.884s Imgs/s=329.59\n",
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"Mode=test: NumWorkers= 4 BatchSize=16 Time= 2.029s Imgs/s=630.98\n",
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"Mode=test: NumWorkers= 4 BatchSize=32 Time= 1.819s Imgs/s=703.63\n",
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"Mode=test: NumWorkers= 8 BatchSize= 1 Time=33.488s Imgs/s= 38.22\n",
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"Mode=test: NumWorkers= 8 BatchSize= 2 Time=16.172s Imgs/s= 79.15\n",
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"Mode=test: NumWorkers= 8 BatchSize= 4 Time= 7.842s Imgs/s=163.22\n",
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"Mode=test: NumWorkers= 8 BatchSize= 8 Time= 3.866s Imgs/s=331.12\n",
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"Mode=test: NumWorkers= 8 BatchSize=16 Time= 2.034s Imgs/s=629.43\n",
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"Mode=test: NumWorkers= 8 BatchSize=32 Time= 1.469s Imgs/s=871.30\n"
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]
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}
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],
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"source": [
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"import torch\n",
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"import torch.nn as nn\n",
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"from torch.utils.data import DataLoader\n",
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"\n",
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"import torchvision.models as models\n",
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"import torchvision.datasets as datasets\n",
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"import torchvision.transforms as transforms\n",
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"import time\n",
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"\n",
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"def main():\n",
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" mode = 'test'\n",
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" model = models.resnet50()\n",
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" criterion = nn.CrossEntropyLoss()\n",
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" optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)\n",
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" N = 1280\n",
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" dataset = datasets.FakeData(size=N, transform=transforms.ToTensor())\n",
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" if mode=='test': # switch to evaluate mode\n",
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" model.eval()\n",
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" model.to('cuda')\n",
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" for num_workers in [1, 2, 4, 8]: # 4 < 2 for test\n",
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" for batch_size in [1, 2, 4, 8, 16, 32]:\n",
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" loader = DataLoader(dataset, num_workers=num_workers, batch_size=batch_size, pin_memory=True)\n",
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" if mode=='test':\n",
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" for i, (data, target) in enumerate(loader):\n",
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" if i==1:\n",
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" tm = time.time()\n",
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" data = data.to('cuda', non_blocking=True)\n",
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" output = model(data)\n",
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" else: # mode=='train':\n",
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" for i, (data, target) in enumerate(loader):\n",
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" if i==1:\n",
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" tm = time.time()\n",
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" data = data.to('cuda', non_blocking=True)\n",
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" target = target.to('cuda', non_blocking=True).long()\n",
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" optimizer.zero_grad()\n",
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" output = model(data)\n",
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" loss = criterion(output, target)\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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" tm = time.time() - tm\n",
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" print('Mode=%s: NumWorkers=%2d BatchSize=%2d Time=%6.3fs Imgs/s=%6.2f' % (mode, num_workers, batch_size, tm, N/tm))\n",
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" torch.cuda.empty_cache() # doesn't seem to be working...\n",
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"\n",
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"if __name__ == '__main__':\n",
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" main()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Mode=test: NumWorkers= 1 BatchSize=40 Time= 7.026s Imgs/s=182.17\n",
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"Mode=test: NumWorkers= 2 BatchSize=40 Time= 3.407s Imgs/s=375.71\n",
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"Mode=test: NumWorkers= 4 BatchSize=40 Time= 1.752s Imgs/s=730.46\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.323s Imgs/s=967.16\n",
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"Mode=test: NumWorkers=16 BatchSize=40 Time= 1.419s Imgs/s=901.91\n"
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]
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}
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],
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"source": [
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"import torch\n",
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"import torch.nn as nn\n",
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"from torch.utils.data import DataLoader\n",
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"\n",
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"import torchvision.models as models\n",
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"import torchvision.datasets as datasets\n",
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"import torchvision.transforms as transforms\n",
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"import time\n",
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"\n",
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"def main():\n",
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" mode = 'test'\n",
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" model = models.resnet50()\n",
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" criterion = nn.CrossEntropyLoss()\n",
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" optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)\n",
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" N = 1280\n",
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" dataset = datasets.FakeData(size=N, transform=transforms.ToTensor())\n",
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" if mode=='test': # switch to evaluate mode\n",
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" model.eval()\n",
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" model.to('cuda')\n",
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" for num_workers in [1, 2, 4, 8, 16]: # 4 < 2 for test\n",
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" for batch_size in [40]:\n",
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" loader = DataLoader(dataset, num_workers=num_workers, batch_size=batch_size, pin_memory=True)\n",
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" if mode=='test':\n",
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" for i, (data, target) in enumerate(loader):\n",
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" if i==1:\n",
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" tm = time.time()\n",
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" data = data.to('cuda', non_blocking=True)\n",
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" output = model(data)\n",
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" else: # mode=='train':\n",
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" for i, (data, target) in enumerate(loader):\n",
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" if i==1:\n",
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" tm = time.time()\n",
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" data = data.to('cuda', non_blocking=True)\n",
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" target = target.to('cuda', non_blocking=True).long()\n",
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" optimizer.zero_grad()\n",
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" output = model(data)\n",
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" loss = criterion(output, target)\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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" tm = time.time() - tm\n",
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" print('Mode=%s: NumWorkers=%2d BatchSize=%2d Time=%6.3fs Imgs/s=%6.2f' % (mode, num_workers, batch_size, tm, N/tm))\n",
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" torch.cuda.empty_cache() # doesn't seem to be working...\n",
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"\n",
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"if __name__ == '__main__':\n",
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" main()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"torch.cuda.empty_cache()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.509s Imgs/s=848.26\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.310s Imgs/s=976.73\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.348s Imgs/s=949.28\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.324s Imgs/s=966.43\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.348s Imgs/s=949.28\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.362s Imgs/s=939.55\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.415s Imgs/s=904.46\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.314s Imgs/s=973.77\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.445s Imgs/s=885.73\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.417s Imgs/s=903.18\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.415s Imgs/s=904.46\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.432s Imgs/s=893.75\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.553s Imgs/s=824.29\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.328s Imgs/s=963.53\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.498s Imgs/s=854.48\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.394s Imgs/s=918.04\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.531s Imgs/s=836.11\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.375s Imgs/s=930.69\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.401s Imgs/s=913.47\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.391s Imgs/s=920.02\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.328s Imgs/s=963.53\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.328s Imgs/s=963.53\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.431s Imgs/s=894.37\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.326s Imgs/s=964.98\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.386s Imgs/s=923.33\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.329s Imgs/s=962.81\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.459s Imgs/s=877.25\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.427s Imgs/s=896.87\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.441s Imgs/s=888.18\n",
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"Mode=test: NumWorkers= 8 BatchSize=40 Time= 1.448s Imgs/s=883.90\n"
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]
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}
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],
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"source": [
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"import torch\n",
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"import torch.nn as nn\n",
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"from torch.utils.data import DataLoader\n",
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"\n",
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"import torchvision.models as models\n",
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"import torchvision.datasets as datasets\n",
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"import torchvision.transforms as transforms\n",
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"import time\n",
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"\n",
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"def main():\n",
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" mode = 'test'\n",
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" model = models.resnet50()\n",
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" criterion = nn.CrossEntropyLoss()\n",
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" optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)\n",
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" N = 1280\n",
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" dataset = datasets.FakeData(size=N, transform=transforms.ToTensor())\n",
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" if mode=='test': # switch to evaluate mode\n",
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" model.eval()\n",
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" model.to('cuda')\n",
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" for _ in range (30):\n",
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" num_workers = 8\n",
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" batch_size = 40\n",
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" loader = DataLoader(dataset, num_workers=num_workers, batch_size=batch_size, pin_memory=True)\n",
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" if mode=='test':\n",
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" for i, (data, target) in enumerate(loader):\n",
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" if i==1:\n",
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" tm = time.time()\n",
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" data = data.to('cuda', non_blocking=True)\n",
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" output = model(data)\n",
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" else: # mode=='train':\n",
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" for i, (data, target) in enumerate(loader):\n",
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" if i==1:\n",
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" tm = time.time()\n",
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" data = data.to('cuda', non_blocking=True)\n",
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" target = target.to('cuda', non_blocking=True).long()\n",
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" optimizer.zero_grad()\n",
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" output = model(data)\n",
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" loss = criterion(output, target)\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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" tm = time.time() - tm\n",
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" print('Mode=%s: NumWorkers=%2d BatchSize=%2d Time=%6.3fs Imgs/s=%6.2f' % (mode, num_workers, batch_size, tm, N/tm))\n",
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" torch.cuda.empty_cache() # doesn't seem to be working...\n",
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"\n",
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"if __name__ == '__main__':\n",
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" main()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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