diff --git a/examples/imagenet_00.py b/examples/imagenet_00.py
new file mode 100644
index 0000000..29f0295
--- /dev/null
+++ b/examples/imagenet_00.py
@@ -0,0 +1,279 @@
+import logging
+import os
+import random
+import shutil
+import time
+import warnings
+
+import hydra
+from omegaconf import DictConfig
+import torch
+import torch.nn as nn
+import torch.backends.cudnn as cudnn
+import torch.distributed as dist
+import torchvision.models as models
+import torchvision.datasets as datasets
+import torchvision.transforms as transforms
+from torch.optim import SGD
+
+logger = logging.getLogger("ImageNet")
+
+
+@hydra.main(config_name="imagenetconf")
+def main(cfg: DictConfig):
+    if cfg.seed is not None:
+        random.seed(cfg.seed)
+        torch.manual_seed(cfg.seed)
+        cudnn.benchmark = False
+        cudnn.deterministic = True
+    if cfg.gpu is not None:
+        logger.info(f"Use GPU: {cfg.gpu} for training")
+    os.environ["MASTER_ADDR"] = "localhost"
+    os.environ["MASTER_PORT"] = "12345"
+    dist.init_process_group(
+        backend=cfg.dist_backend,
+        # init_method=cfg.dist_url,
+        world_size=cfg.world_size,
+        rank=cfg.gpu,
+    )
+    return
+
+    model = models.__dict__[cfg.arch]()
+    torch.cuda.set_device(cfg.gpu)
+    model.cuda(cfg.gpu)
+    # When using a single GPU per process and per
+    # DistributedDataParallel, we need to divide the batch size
+    # ourselves based on the total number of GPUs we have
+    cfg.batch_size = int(cfg.batch_size / cfg.world_size)
+    model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[cfg.gpu])
+
+    criterion = nn.CrossEntropyLoss().cuda(cfg.gpu)
+    optimizer = SGD(
+        params=model.parameters(),
+        lr=cfg.sgd.lr,
+        lambd=cfg.sgd.lambd,
+        alpha=cfg.sgd.alpha,
+        t0=cfg.sgd.t0,
+        weight_decay=cfg.sgd.weight_decay,
+    )
+
+    traindir = os.path.join(cfg.data, "train")
+    valdir = os.path.join(cfg.data, "val")
+    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 cfg.distributed:
+        train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
+    else:
+        train_sampler = None
+
+    train_loader = torch.utils.data.DataLoader(
+        train_dataset,
+        batch_size=cfg.batch_size,
+        shuffle=(train_sampler is None),
+        num_workers=cfg.workers,
+        pin_memory=True,
+        sampler=train_sampler,
+    )
+
+    val_loader = torch.utils.data.DataLoader(
+        datasets.ImageFolder(
+            valdir,
+            transforms.Compose(
+                [
+                    transforms.Resize(256),
+                    transforms.CenterCrop(224),
+                    transforms.ToTensor(),
+                    normalize,
+                ]
+            ),
+        ),
+        batch_size=cfg.batch_size,
+        shuffle=False,
+        num_workers=cfg.workers,
+        pin_memory=True,
+    )
+
+    if cfg.evaluate:
+        validate(val_loader, model, criterion, cfg)
+        return
+
+    for epoch in range(cfg.start_epoch, cfg.epochs):
+        if cfg.distributed:
+            train_sampler.set_epoch(epoch)
+        adjust_learning_rate(optimizer, epoch, cfg)
+
+        train(train_loader, model, criterion, optimizer, epoch, cfg)
+        validate(val_loader, model, criterion, cfg)
+
+
+def train(train_loader, model, criterion, optimizer, epoch, cfg):
+    batch_time = AverageMeter("Time", ":6.3f")
+    data_time = AverageMeter("Data", ":6.3f")
+    losses = AverageMeter("Loss", ":.4e")
+    top1 = AverageMeter("Acc@1", ":6.2f")
+    top5 = AverageMeter("Acc@5", ":6.2f")
+    progress = ProgressMeter(
+        len(train_loader),
+        [batch_time, data_time, losses, top1, top5],
+        prefix="Epoch: [{}]".format(epoch),
+    )
+
+    # switch to train mode
+    model.train()
+
+    end = time.time()
+    for i, (images, target) in enumerate(train_loader):
+        # measure data loading time
+        data_time.update(time.time() - end)
+
+        if cfg.gpu is not None:
+            images = images.cuda(cfg.gpu, non_blocking=True)
+        if torch.cuda.is_available():
+            target = target.cuda(cfg.gpu, non_blocking=True)
+
+        # compute output
+        output = model(images)
+        loss = criterion(output, target)
+
+        # measure accuracy and record loss
+        acc1, acc5 = accuracy(output, target, topk=(1, 5))
+        losses.update(loss.item(), images.size(0))
+        top1.update(acc1[0], images.size(0))
+        top5.update(acc5[0], images.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 % cfg.print_freq == 0:
+            progress.display(i)
+
+
+def validate(val_loader, model, criterion, cfg):
+    batch_time = AverageMeter("Time", ":6.3f")
+    losses = AverageMeter("Loss", ":.4e")
+    top1 = AverageMeter("Acc@1", ":6.2f")
+    top5 = AverageMeter("Acc@5", ":6.2f")
+    progress = ProgressMeter(
+        len(val_loader), [batch_time, losses, top1, top5], prefix="Test: "
+    )
+
+    # switch to evaluate mode
+    model.eval()
+
+    with torch.no_grad():
+        end = time.time()
+        for i, (images, target) in enumerate(val_loader):
+            if cfg.gpu is not None:
+                images = images.cuda(cfg.gpu, non_blocking=True)
+            if torch.cuda.is_available():
+                target = target.cuda(cfg.gpu, non_blocking=True)
+
+            # compute output
+            output = model(images)
+            loss = criterion(output, target)
+
+            # measure accuracy and record loss
+            acc1, acc5 = accuracy(output, target, topk=(1, 5))
+            losses.update(loss.item(), images.size(0))
+            top1.update(acc1[0], images.size(0))
+            top5.update(acc5[0], images.size(0))
+
+            # measure elapsed time
+            batch_time.update(time.time() - end)
+            end = time.time()
+
+            if i % cfg.print_freq == 0:
+                progress.display(i)
+        logger.info(
+            " * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}".format(top1=top1, top5=top5)
+        )
+
+
+class AverageMeter(object):
+    """Computes and stores the average and current value"""
+
+    def __init__(self, name, fmt=":f"):
+        self.name = name
+        self.fmt = fmt
+        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 __str__(self):
+        fmtstr = "{name} {val" + self.fmt + "} ({avg" + self.fmt + "})"
+        return fmtstr.format(**self.__dict__)
+
+
+class ProgressMeter(object):
+    def __init__(self, num_batches, meters, prefix=""):
+        self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
+        self.meters = meters
+        self.prefix = prefix
+
+    def display(self, batch):
+        entries = [self.prefix + self.batch_fmtstr.format(batch)]
+        entries += [str(meter) for meter in self.meters]
+        logger.info("\t".join(entries))
+
+    def _get_batch_fmtstr(self, num_batches):
+        num_digits = len(str(num_batches // 1))
+        fmt = "{:" + str(num_digits) + "d}"
+        return "[" + fmt + "/" + fmt.format(num_batches) + "]"
+
+
+def adjust_learning_rate(optimizer, epoch, cfg):
+    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
+    lr = cfg.lr * (0.1 ** (epoch // 30))
+    for param_group in optimizer.param_groups:
+        param_group["lr"] = lr
+
+
+def accuracy(output, target, topk=(1,)):
+    """Computes the accuracy over the k top predictions for the specified values of k"""
+    with torch.no_grad():
+        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].reshape(-1).float().sum(0, keepdim=True)
+            res.append(correct_k.mul_(100.0 / batch_size))
+        return res
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/imagenetconf.yaml b/examples/imagenetconf.yaml
new file mode 100644
index 0000000..5fab571
--- /dev/null
+++ b/examples/imagenetconf.yaml
@@ -0,0 +1,17 @@
+seed: ~
+gpu: ~
+world_size: 4
+dist_backend: nccl
+dist_url: "localhost:9999"
+batch_size: 256
+ngpus_per_node: 4
+sgd:
+    lr: 0.01
+    lambd: 0.0001
+    alpha: 0.75
+    t0: 1000000.0
+    weight_decay: 0
+
+
+defaults:
+    - hydra/launcher: joblib