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[korisnik@x3000c0s25b0n0] $ module load scientific/pytorch/1.14.0-ngc
[korisnik@x3000c0s25b0n0] $ run-command.sh pip3 list
INFO:    underlay of /etc/localtime required more than 50 (95) bind mounts
INFO:    underlay of /usr/bin/nvidia-smi required more than 50 (474) bind mounts
13:4: not a valid test operator: (
13:4: not a valid test operator: 510.47.03
Package                 Version
----------------------- -------------------------------
absl-py                 1.3.0
accelerate              0.19.0
apex                    0.1
appdirs                 1.4.4
argon2-cffi             21.3.0
argon2-cffi-bindings    21.2.0
asttokens               2.2.1
...

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

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 = 256
    samples = 256*100
    epochs = 1

    # model
    model = resnet50(weights=None)
    model.cuda()
    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):
            images = images.cuda()
            labels = labels.cuda()
            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()