# Low GPU Usage during Training

**URL:** <https://discuss.pytorch.org/t/low-gpu-usage-during-training/121588>\
**Category:** Uncategorized\
**Created:** [May 18, 2021, 4:44am UTC](https://discuss.pytorch.org/t/low-gpu-usage-during-training/121588 "2021-05-18T04:44:05Z")\
**Posts on this page:** 1\
**Showing post:** 21

<div class="post-metadata">

**Author:** ![jmandivarapu1](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/jmandivarapu1/32/3544_2.png) [@jmandivarapu1](https://discuss.pytorch.org/u/jmandivarapu1)\
**Post date:** [May 18, 2021, 3:57pm UTC](https://discuss.pytorch.org/t/low-gpu-usage-during-training/121588/21 "2021-05-18T15:57:55Z")

</div>

Unfortunately I think I can’t help much. As the file in your github running successfully for me for different batch sizes and differ workers also. My gpu utilization is around 22%. But I am using linux machine and I do have 32 cpu’s in it.

But I did found one interesting article about num\_wprkers\>0 on windows mightnot work and how to fix it  
solution is  
( [Errors when using num\_workers\>0 in DataLoader - PyTorch Forums](https://discuss.pytorch.org/t/errors-when-using-num-workers-0-in-dataloader/97564/4))

```auto
def train():
    # Here was inserted the whole code that train the network ...
if __name__ == ' __main__':
    train()

```

So I tried modifying your files but as I mentioned I don’t have windows system so try my below code in MNIST.py file if doesn’t work please follow the above link

```auto
import numpy as np
import torch
import torch.nn as nn
import torchvision

from torchvision.transforms import ToTensor
from torch.utils.data import Dataset
from torchvision.utils import make_grid
from torch.utils.data import random_split

import matplotlib.pyplot as plt

from model import MnistMLP,MnistCNN
from trainer import TrainerConfig,Trainer
from visualize import Plot

train_set = torchvision.datasets.MNIST(
    root="./data",
    train=True,
    download=True,
    transform=ToTensor()
)

test_set = torchvision.datasets.MNIST(
    root="./data",
    train=False,
    download=True,
    transform=ToTensor()
)

if __name__ == ' __main__': 
    cnn_train_configs = TrainerConfig(ckpt_path="./CNNModel.pt",max_epochs=40,learning_rate=4.67e-4,weight_decay=6.423e-4)
    CNN_model = MnistCNN()
    trainer = Trainer(model=CNN_model,train_dataset=train_set,test_dataset=test_set,config=cnn_train_configs)
    model_metrics = trainer.train()

    plotter = Plot(model_metrics=model_metrics)
    plotter.plot()

```

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