Great mahn! It worked with Kaggle T4x2

[Jaykumaran]

%%writefile train.py

import os
import time
import datetime

import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import torch.backends.cudnn as cudnn

import torchvision
import torchvision.transforms as transforms
from torchvision.datasets import CIFAR10
from torch.utils.data import DataLoader

import torch.distributed as dist
import torch.multiprocessing as mp
import torch.utils.data.distributed

# from model import pyramidnet
import argparse
from tensorboardX import SummaryWriter


parser = argparse.ArgumentParser(description='cifar10 classification models')
parser.add_argument('--lr', default=0.1, help='')
parser.add_argument('--resume', default=None, help='')
parser.add_argument('--batch_size', type=int, default=768, help='')
parser.add_argument('--num_workers', type=int, default=4, help='')
parser.add_argument("--gpu_devices", type=int, nargs='+', default=None, help="")

parser.add_argument('--gpu', default=None, type=int, help='GPU id to use.')
parser.add_argument('--dist-url', default='tcp://127.0.0.1:3456', type=str, help='')
parser.add_argument('--dist-backend', default='nccl', type=str, help='')
parser.add_argument('--rank', default=0, type=int, help='')
parser.add_argument('--world_size', default=1, type=int, help='')
parser.add_argument('--distributed', action='store_true', help='')
args = parser.parse_args()

gpu_devices = ','.join([str(id) for id in args.gpu_devices])
os.environ["CUDA_VISIBLE_DEVICES"] = gpu_devices


model = torchvision.models.resnet18(weights = 'DEFAULT')
model.fc = nn.Linear(model.fc.in_features, 10)

# summary(model, (1, 3, 224, 224), device = "cpu")
# model.to(device) # move to gpu
print("--- Model Loaded --- ")


def main():
    args = parser.parse_args()

    ngpus_per_node = torch.cuda.device_count()

    args.world_size = ngpus_per_node * args.world_size
    mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args))
        
        
def main_worker(gpu, ngpus_per_node, args):
    args.gpu = gpu
    ngpus_per_node = torch.cuda.device_count()    
    print("Use GPU: {} for training".format(args.gpu))
        
    args.rank = args.rank * ngpus_per_node + gpu    
    dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
                            world_size=args.world_size, rank=args.rank)

    print('==> Making model..')
    net = model
    torch.cuda.set_device(args.gpu)
    net.cuda(args.gpu)
    args.batch_size = int(args.batch_size / ngpus_per_node)
    args.num_workers = int(args.num_workers / ngpus_per_node)
    net = torch.nn.parallel.DistributedDataParallel(net, device_ids=[args.gpu])
    num_params = sum(p.numel() for p in net.parameters() if p.requires_grad)
    print('The number of parameters of model is', num_params)

    print('==> Preparing data..')
    transforms_train = transforms.Compose([
        transforms.RandomCrop(32, padding=4),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])

    dataset_train = CIFAR10(root='./data', train=True, download=True, 
                            transform=transforms_train)
    train_sampler = torch.utils.data.distributed.DistributedSampler(dataset_train)
    train_loader = DataLoader(dataset_train, batch_size=args.batch_size, 
                              shuffle=(train_sampler is None), num_workers=args.num_workers, 
                              sampler=train_sampler)

    # there are 10 classes so the dataset name is cifar-10
    classes = ('plane', 'car', 'bird', 'cat', 'deer', 
               'dog', 'frog', 'horse', 'ship', 'truck')
    
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(net.parameters(), lr=args.lr, 
                          momentum=0.9, weight_decay=1e-4)
    
    train(net, criterion, optimizer, train_loader, args.gpu)
            

def train(net, criterion, optimizer, train_loader, device):
    net.train()

    train_loss = 0
    correct = 0
    total = 0
    
    epoch_start = time.time()
    for batch_idx, (inputs, targets) in enumerate(train_loader):
        start = time.time()
        
        inputs = inputs.cuda(device)
        targets = targets.cuda(device)
        outputs = net(inputs)
        loss = criterion(outputs, targets)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        train_loss += loss.item()
        _, predicted = outputs.max(1)
        total += targets.size(0)
        correct += predicted.eq(targets).sum().item()

        acc = 100 * correct / total
        
        batch_time = time.time() - start
        
        if batch_idx % 20 == 0:
            print('Epoch: [{}/{}]| loss: {:.3f} | acc: {:.3f} | batch time: {:.3f}s '.format(
                batch_idx, len(train_loader), train_loss/(batch_idx+1), acc, batch_time))
    
    elapse_time = time.time() - epoch_start
    elapse_time = datetime.timedelta(seconds=elapse_time)
    print("Training time {}".format(elapse_time))
    

if __name__=='__main__':
    main()

!python train.py --gpu_device 0 1 2 3 --batch_size 768

After lot of struggles, this helped me a lot; My objective is to make use to DDP in Kaggle T4x2

[dnddnjs]

I am happy hear that!