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Pytorch MNIST example script

You can launch the batch job (single GPU version) via:

cd jean-zay-doc/docs/examples/pytorch/mnist/
sbatch ./

Alternatively, a multi GPU version is available. It launches the training with 10 different values for a single parameter. In SLURM language this is called a job array.

This script implements a kind of parallelism (when data is not shared between different jobs). It can be useful to optimize the values of the hyperparameters during the training:

cd jean-zay-doc/docs/examples/pytorch/mnist/
sbatch ./

This is the example script (

# Taken from the official repository examples: 

from __future__ import print_function
import argparse
import os
import os.path
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout2d(0.25)
        self.dropout2 = nn.Dropout2d(0.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = F.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        output = F.log_softmax(x, dim=1)
        return output

def train(args, model, device, train_loader, optimizer, epoch):
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target =,
        output = model(data)
        loss = F.nll_loss(output, target)
        if batch_idx % args.log_interval == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))

def test(args, model, device, test_loader):
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target =,
            output = model(data)
            test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss
            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)

    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))

def main():
    # Training settings
    parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
    parser.add_argument('--batch-size', type=int, default=64, metavar='N',
                        help='input batch size for training (default: 64)')
    parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
                        help='input batch size for testing (default: 1000)')
    parser.add_argument('--epochs', type=int, default=14, metavar='N',
                        help='number of epochs to train (default: 14)')
    parser.add_argument('--lr', type=float, default=1.0, metavar='LR',
                        help='learning rate (default: 1.0)')
    parser.add_argument('--gamma', type=float, default=0.7, metavar='M',
                        help='Learning rate step gamma (default: 0.7)')
    parser.add_argument('--no-cuda', action='store_true', default=False,
                        help='disables CUDA training')
    parser.add_argument('--seed', type=int, default=1, metavar='S',
                        help='random seed (default: 1)')
    parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                        help='how many batches to wait before logging training status')

    parser.add_argument('--save-model', action='store_true', default=False,
                        help='For Saving the current Model')
    args = parser.parse_args()
    use_cuda = not args.no_cuda and torch.cuda.is_available()


    device = torch.device("cuda" if use_cuda else "cpu")

    kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
    train_loader =
        datasets.MNIST(os.environ['DSDIR'] , train=True, download=False,
                           transforms.Normalize((0.1307,), (0.3081,))
        batch_size=args.batch_size, shuffle=True, **kwargs)
    test_loader =
        datasets.MNIST(os.environ['DSDIR'], train=False, transform=transforms.Compose([
                           transforms.Normalize((0.1307,), (0.3081,))
        batch_size=args.test_batch_size, shuffle=True, **kwargs)

    model = Net().to(device)
    optimizer = optim.Adadelta(model.parameters(),

    scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
    for epoch in range(1, args.epochs + 1):
        train(args, model, device, train_loader, optimizer, epoch)
        test(args, model, device, test_loader)

    if args.save_model:, "")

if __name__ == '__main__':

And the launching script for a single GPU version (

#SBATCH --job-name=pytorch_mnist     # job name
#SBATCH --ntasks=1                   # number of MP tasks
#SBATCH --ntasks-per-node=1          # number of MPI tasks per node
#SBATCH --gres=gpu:1                 # number of GPUs per node
#SBATCH --cpus-per-task=10           # number of cores per tasks
#SBATCH --hint=nomultithread         # we get physical cores not logical
#SBATCH --distribution=block:block   # we pin the tasks on contiguous cores
#SBATCH --time=3:00:00              # maximum execution time (HH:MM:SS)
#SBATCH --output=pytorch_mnist%j.out # output file name
#SBATCH --error=pytorch_mnist%j.err  # error file name

set -x

module purge
module load pytorch-gpu/py3/1.4.0 

python ./