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Ispod se nalazi primjer aplikacije umjetnog benchmarka koji testira performanse na modelu Resnet50.
Aplikacija na jednom čvoru
| Code Block |
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| language | bash |
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| title | singlenode.sh |
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| linenumbers | true |
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| collapse | true |
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#PBS -q cpu
#PBS -l ncpus=32
#PBS -l mem=50GB
# environment
module load scientific/pytorch/2.0.0
# set thread number to the cpu one
export OMP_NUM_THREADS=${NCPUS}
# run
cd ${PBS_O_WORKDIR:-""}
python singlenode.py |
| Code Block |
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| language | py |
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| title | singlenode.py |
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| linenumbers | true |
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| collapse | true |
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import os
import time
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision.models import resnet50
from torchvision.datasets import FakeData
from torchvision.transforms import ToTensor
def main():
# vars
batch = 16
samples = 16*30
epochs = 3
# model
model = resnet50(weights=None)
optimizer = optim.SGD(model.parameters(), lr=0.001)
loss_fn = nn.CrossEntropyLoss()
# data
dataset = FakeData(samples,
num_classes=1000,
transform=ToTensor())
loader = DataLoader(dataset,
batch_size=batch,
shuffle=False,
num_workers=1,
pin_memory=True)
# train
for epoch in range(epochs):
start = time.time()
for batch, (images, labels) in enumerate(loader):
outputs = model(images)
classes = torch.argmax(outputs, dim=1)
loss = loss_fn(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (batch%10 == 0):
print('--- Epoch %i, Batch %3i / %3i, Loss = %0.2f ---' % (epoch,
batch,
len(loader),
loss.item()))
elapsed = time.time()-start
imgsec = samples/elapsed
print('--- Epoch %i finished: %0.2f img/sec ---' % (epoch,
imgsec))
if __name__ == "__main__":
main() |
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