# source
# - https://github.com/horovod/horovod/blob/master/examples/pytorch/pytorch_synthetic_benchmark.py

import argparseos
import time

import torch.backends.cudnn as cudnn
import torch.nn.functional as Fnn
import torch.optim as optim
import
from torch.utils.data.distributed import DataLoader

from torchvision.models import models

import sys
import time
import numpy as np

# Benchmark settings
parser = argparse.ArgumentParser(description='PyTorch Synthetic Benchmark',resnet50
from torchvision.datasets import FakeData
from torchvision.transforms import ToTensor

def main():

    # vars
    batch = 256
    samples = 256*100
    epochs = 1

    # model
    model = resnet50(weights=None)
    formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("-i",model.cuda()
    optimizer = optim.SGD(model.parameters(), lr=0.001)
    loss_fn = nn.CrossEntropyLoss()

    # data
    dataset = FakeData(samples,
   "--images",
                    typenum_classes=int1000,
                       help="image number",
transform=ToTensor())
    loader = DataLoader(dataset,
                     default=1024)
parser.add_argument('--   batch_size'=batch,
                       type shuffle=intFalse,
                    default=32    num_workers=1,
                    help='input batch size')
parser.add_argument("-e",    pin_memory=True)

    # train
    for epoch in range(epochs):
        "--epochs",start = time.time()
        for batch, (images, labels) in enumerate(loader):
       type=int,
     images = images.cuda()
            labels help="epochs",
= labels.cuda()
            outputs = model(images)
       default=1)
parser.add_argument('--model',     classes = torch.argmax(outputs, dim=1)
            loss = loss_fn(outputs, labels)
          type=str,
  optimizer.zero_grad()
            loss.backward()
      default='resnet50',
      optimizer.step()
            if (batch%10 help=='model to benchmark')
args = parser.parse_args()

# model
model = getattr(models, args.model)()
model.cuda()

lr_scaler = 1
optimizer = optim.SGD(model.parameters(), lr=0.01 * lr_scaler)

cudnn.benchmark = True

# data
data = torch.randn(args.batch_size, 3, 224, 224)
target = torch.LongTensor(args.batch_size).random_() % 1000
data, target = data.cuda(), target.cuda()

# fit
def benchmark_step():
    optimizer.zero_grad()
    output = model(data)
    loss = F.cross_entropy(output, target)
    loss.backward()
    optimizer.step()
    return loss.item()

for epoch in range(args.epochs):
    begin = time.time()
    for batches in range(args.images//args.batch_size):
 0):
                print('--- Epoch %i, Batch %3i / %3i, Loss = %0.2f ---' % (epoch,
                                                                           batch,
                                                           loss = benchmark_step()
        if (batches%10 == 0):
   len(loader),
         print('--- Epoch %2i, Batch %3i: Loss = %0.2f ---' % (epoch,
                                                        loss.item()))
        elapsed  batches,
= time.time()-start
        imgsec = samples/elapsed
        print('--- Epoch %i finished: %0.2f img/sec ---' % (epoch,
                                            loss,))
    end = time.time()
    imgsec = args.images//(end-begin)
    print('--- Epoch %2i finished: %0.2f img/sec ---' % (epoch, imgsec)imgsec))

if __name__ == "__main__":
    main()

#!/bin/bash

#PBS -q gpu
#PBS -l select=8:ngpus=1:ncpus=4

# pozovi module
module load scientific/pytorch/1.14.0	-ngc
module load cray-pals

# pomakni se u direktorij gdje se nalazi skripta
cd ${PBS_O_WORKDIR:-""}

# potjeraj skriptu korištenjem torchrun-multinode.sh
torchrun-multinode.sh multigpu-multinode.py