Torch seed doesn't work. l can't reprodeuce my results

DeepLearner17: <p>Hello,</p>
<p>l trained my model on ONE GPU, a set seed as as follow in my main program :</p>
<pre><code class="lang-auto">    seed=15
    torch.manual_seed(seed)
    np.random.RandomState(seed)
</code></pre>
<p>However, l can’t reproduce my results.  What is wrong ?</p>
<p>Here is my code  :</p>
<pre><code class="lang-auto">model_names = sorted(name for name in models.__dict__
    if name.islower() and not name.startswith("__")
    and callable(models.__dict__[name]))

parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
'''
parser.add_argument('data_train', metavar='DIR',
                    help='path to dataset train')
parser.add_argument('data_valid', metavar='DIR',
                    help='path to dataset valid')
'''
parser.add_argument('--arch', '-a', metavar='ARCH', default='resnet18',
                    choices=model_names,
                    help='model architecture: ' +
                        ' | '.join(model_names) +
                        ' (default: resnet152)')
parser.add_argument('-j', '--workers', default=1, type=int, metavar='N',
                    help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=100, type=int, metavar='N',
                    help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
                    help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch-size', default=10, type=int,
                    metavar='N', help='mini-batch size (default: 256)')
parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
                    metavar='LR', help='initial learning rate')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                    help='momentum')
parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float,
                    metavar='W', help='weight decay (default: 1e-4)')
parser.add_argument('--print-freq', '-p', default=1, type=int,
                    metavar='N', help='print frequency (default: 10)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
                    help='path to latest checkpoint (default: none)')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
                    help='evaluate model on validation set')
parser.add_argument('--pretrained', dest='pretrained', action='store_true',
                    help='use pre-trained model')
parser.add_argument('--world-size', default=1, type=int,
                    help='number of distributed processes')
parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str,
                    help='url used to set up distributed training')
parser.add_argument('--dist-backend', default='gloo', type=str,
                    help='distributed backend')

best_prec1 = 0

def main():

    seed=15
    torch.manual_seed(seed)
    #torch.cuda.manual_seed(seed)
    #torch.cuda.manual_seed_all(seed)
    np.random.RandomState(seed)
    global args, best_prec1
    args = parser.parse_args()

    args.distributed = args.world_size &gt; 1

    my_model_name = 'resnet152 pretrained'
    print(my_model_name)

    with open('train.sav', 'rb') as handle:
        train_data = pickle.load(handle)


    with open('test.sav', 'rb') as handle:
        valid_data = pickle.load(handle)

    print('now training '+ str(my_model_name))




    if args.distributed:
        dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
                                world_size=args.world_size)

    # create model
    if args.pretrained:
        print("=&gt; using pre-trained model '{}'".format(args.arch))
        model = models.__dict__[args.arch](pretrained=True)
    else:
        print("=&gt; creating model '{}'".format(args.arch))
        model = models.__dict__[args.arch]()

    if not args.distributed:
        if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
            model.features = torch.nn.DataParallel(model.features)
            model.cuda()
        else:
            model = torch.nn.DataParallel(model).cuda()
    else:
        model.cuda()
        model = torch.nn.parallel.DistributedDataParallel(model)

    # define loss function (criterion) and optimizer
    criterion = nn.CrossEntropyLoss().cuda()

    optimizer = torch.optim.SGD(model.parameters(), args.lr,
                                momentum=args.momentum,
                                weight_decay=args.weight_decay)

    # optionally resume from a checkpoint
    if args.resume:
        if os.path.isfile(args.resume):
            print("=&gt; loading checkpoint '{}'".format(args.resume))
            checkpoint = torch.load(args.resume)
            args.start_epoch = checkpoint['epoch']
            best_prec1 = checkpoint['best_prec1']
            model.load_state_dict(checkpoint['state_dict'])
            optimizer.load_state_dict(checkpoint['optimizer'])
            print("=&gt; loaded checkpoint '{}' (epoch {})"
                  .format(args.resume, checkpoint['epoch']))
        else:
            print("=&gt; no checkpoint found at '{}'".format(args.resume))

    cudnn.benchmark = True


    traindir =train_data
    valdir = valid_data
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])
    '''
    train_dataset = datasets.ImageFolder(
        traindir,
        transforms.Compose([
            transforms.RandomResizedCrop(224),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            normalize,
        ]))
    '''

    if args.distributed:
        train_sampler = torch.utils.data.distributed.DistributedSampler(train)
    else:
        train_sampler = None

    train_loader = torch.utils.data.DataLoader(
        train_data, batch_size=args.batch_size, shuffle=(train_sampler is None),
        num_workers=args.workers, pin_memory=True, sampler=train_sampler)

    val_loader = torch.utils.data.DataLoader(valid_data,
        batch_size=args.batch_size, shuffle=False,
        num_workers=args.workers, pin_memory=True)

    '''
    val_loader = torch.utils.data.DataLoader(
        datasets.ImageFolder(valdir, transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            normalize,
        ])),
        batch_size=args.batch_size, shuffle=False,
        num_workers=args.workers, pin_memory=True)
    '''
    if args.evaluate:
        validate(val_loader, model, criterion)
        return
    d = datetime.now()
    g_epoch = []
    g_train_accuracy = []
    g_valid_accuracy = []
    g_valid_loss=[]
    g_train_loss=[]
    for epoch in range(args.start_epoch, args.epochs):
        if args.distributed:
            train_sampler.set_epoch(epoch)
        adjust_learning_rate(optimizer, epoch)

        # train for one epoch
        prec_train,loss_train=train(train_loader, model, criterion, optimizer, epoch)

        # evaluate on validation set
        prec1,loss_val = validate(val_loader, model, criterion)

        # remember best prec@1 and save checkpoint
        is_best = prec1 &gt; best_prec1
        best_prec1 = max(prec1, best_prec1)
        save_checkpoint({
            'epoch': epoch + 1,
            'arch': args.arch,
            'state_dict': model.state_dict(),
            'best_prec1': best_prec1,
            'optimizer' : optimizer.state_dict(),
        }, is_best)
        g_epoch.append(epoch)
        g_train_accuracy.append(prec_train)
        g_train_loss.append(loss_train)
        g_valid_loss.append(loss_val)
        g_valid_accuracy.append(prec1)
        #import ipdb; ipdb.set_trace()

    print('video_images depth1 ucf train2 ' + str(my_model_name))
    fig1, ax1 = plt.subplots()
    #fig2, ax2 = plt.subplots()
    ax1.plot(g_epoch, g_train_accuracy, label='train_accuracy')
    ax1.plot(g_epoch, g_valid_accuracy, label='valid_valid')
    ax1.set_xlabel("epoch")
    ax1.set_ylabel("accuracy")
    ax1.set_title(" accuracy with "+ str(my_model_name))
    ax1.legend(loc='best')
    fig1.savefig('accuracy'+str(my_model_name)+'.png', bbox_inches='tight')
    '''
    ax2.plot(g_epoch, g_valid_loss, label='valid_loss')
    ax2.plot(g_epoch, g_train_loss, label='train_loss')
    ax2.set_xlabel("epoch")
    ax2.set_ylabel("loss")
    ax2.set_title(" loss with "+str(my_model_name))
    ax2.legend(loc='best')

    fig2.savefig('loss'+str(my_model_name)+'.png', bbox_inches='tight')
    '''
    print('video_images depth1 ucf train2 '+str(my_model_name))

    v = datetime.now()
    c=v-d
    print('time ', c)


def train(train_loader, model, criterion, optimizer, epoch):
    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()

    # switch to train mode
    model.train()

    end = time.time()
    for i, (input, target) in enumerate(train_loader):
        # measure data loading time
        data_time.update(time.time() - end)

        target = target.cuda(async=True)
        input_var = torch.autograd.Variable(input)
        target_var = torch.autograd.Variable(target)

        # compute output
        output = model(input_var)
        loss = criterion(output, target_var)

        # measure accuracy and record loss
        prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
        losses.update(loss.data[0], input.size(0))
        top1.update(prec1[0], input.size(0))
        top5.update(prec5[0], input.size(0))

        # compute gradient and do SGD step
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if i % args.print_freq == 0:
            print('Epoch: [{0}][{1}/{2}]\t'
                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
                  'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                  'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                  'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                   epoch, i, len(train_loader), batch_time=batch_time,
                   data_time=data_time, loss=losses, top1=top1, top5=top5))
    return top1.avg, losses

def validate(val_loader, model, criterion):
    batch_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()

    # switch to evaluate mode
    model.eval()

    end = time.time()
    for i, (input, target) in enumerate(val_loader):
        target = target.cuda(async=True)
        input_var = torch.autograd.Variable(input, volatile=True)
        target_var = torch.autograd.Variable(target, volatile=True)

        # compute output
        output = model(input_var)
        loss = criterion(output, target_var)

        # measure accuracy and record loss
        prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
        losses.update(loss.data[0], input.size(0))
        top1.update(prec1[0], input.size(0))
        top5.update(prec5[0], input.size(0))

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if i % args.print_freq == 0:
            print('Test: [{0}/{1}]\t'
                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                  'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                  'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                   i, len(val_loader), batch_time=batch_time, loss=losses,
                   top1=top1, top5=top5))

    print(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'
          .format(top1=top1, top5=top5))

    return top1.avg, losses


def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, 'model_best.pth.tar')


class AverageMeter(object):
    """Computes and stores the average and current value"""
    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count


def adjust_learning_rate(optimizer, epoch):
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    lr = args.lr * (0.1 ** (epoch // 30))
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr


def accuracy(output, target, topk=(1,)):
    """Computes the precision@k for the specified values of k"""
    maxk = max(topk)
    batch_size = target.size(0)

    _, pred = output.topk(maxk, 1, True, True)
    pred = pred.t()
    correct = pred.eq(target.view(1, -1).expand_as(pred))

    res = []
    for k in topk:
        correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
        res.append(correct_k.mul_(100.0 / batch_size))
    return res


if __name__ == '__main__':

   main()
</code></pre>

albanD: <p>Hi,</p>
<p>To get reproducible results on gpu can be tricky, do you do the following already?</p>
<ul>
<li>If you use some randomness on the gpu you need to set <code>torch.cuda.manual_seed(seed)</code> as well.</li>
<li>If you use randomness on severall gpus, you need to set <code>torch.cuda.manual_seed_all(seed)</code>.</li>
<li>If you use cudnn, you need to set <code>torch.backends.cudnn.deterministic=True</code>.</li>
</ul>

DeepLearner17: <p>Hi,</p>
<ol>
<li>Yes on one GPU, l used seed=15<br>
torch.manual_seed(seed). l use only one GPU . However, for instance l run my code on GPU 0 of machine X and l would like to reproduce result on GPU 2 of machine Y. Is it correct to do seed=15<br>
torch.manual_seed(seed) ?</li>
<li>Yes l use cudnn. Where l have to set torch.backends.cudnn.deterministic=True ?</li>
</ol>
<p>Please take a loot at my code</p>

albanD: <ol start="2">
<li>
<p>Set it anywhere before using any cudnn operation. This flag is checked when cudnn operation is made to choose the algorithm that is then being used.</p>
</li>
<li>
<p>On the cpu side, setting the same seed on different machines will give the same results. On gpu, cuda’s random number generator is used and I don’t know if it will give your exactly the same thing when using different cards. Similarly, many cuda operation could give slightly different results when ran on different cards because of the architectural change. So I’m not sure that you will be able to reproduce exactly on a different card.</p>
</li>
</ol>

s.sni: <p>for me the problem related to “numpy” not “torch”. setting seed by the following code solved the problem.</p>
<pre><code class="lang-auto">np.random.seed(15)
</code></pre>
<p>setting seed for “random” package if it is imported is also necessary:</p>
<pre><code class="lang-auto">import random

random.seed(15)
</code></pre>