diff --git a/examples/frameworks/pytorch/pytorch_abseil.py b/examples/frameworks/pytorch/pytorch_abseil.py
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+++ b/examples/frameworks/pytorch/pytorch_abseil.py
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+# Example of MNIST training with PyTorch and abseil integration
+
+from __future__ import print_function
+
+import os
+from tempfile import gettempdir
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from absl import app, flags
+from torchvision import datasets, transforms
+
+from clearml import Task, Logger
+
+# Training settings
+FLAGS = flags.FLAGS
+
+flags.DEFINE_integer("batch_size", 64, help="Batch size for training")
+flags.DEFINE_integer("test_batch_size", 1000, help="Batch size for testing")
+flags.DEFINE_integer("epochs", 10, help="Number of epochs to train")
+flags.DEFINE_float("lr", 0.01, help="Learning rate")
+flags.DEFINE_float("momentum", 0.5, help="SGD momentum")
+flags.DEFINE_bool("cuda", True, help="Enable CUDA training")
+flags.DEFINE_integer("seed", 1, help="Random seed")
+flags.DEFINE_integer(
+    "log_interval", 10, help="How many batches to wait before logging training status"
+)
+flags.DEFINE_bool("save_model", True, help="For saving the current Model")
+
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(1, 20, 5, 1)
+        self.conv2 = nn.Conv2d(20, 50, 5, 1)
+        self.fc1 = nn.Linear(4 * 4 * 50, 500)
+        self.fc2 = nn.Linear(500, 10)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.max_pool2d(x, 2, 2)
+        x = F.relu(self.conv2(x))
+        x = F.max_pool2d(x, 2, 2)
+        x = x.view(-1, 4 * 4 * 50)
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return F.log_softmax(x, dim=1)
+
+
+def train(args, model, device, train_loader, optimizer, epoch):
+    model.train()
+    for batch_idx, (data, target) in enumerate(train_loader):
+        data, target = data.to(device), target.to(device)
+        optimizer.zero_grad()
+        output = model(data)
+        loss = F.nll_loss(output, target)
+        loss.backward()
+        optimizer.step()
+        if batch_idx % args.log_interval == 0:
+            Logger.current_logger().report_scalar(
+                "train",
+                "loss",
+                iteration=(epoch * len(train_loader) + batch_idx),
+                value=loss.item(),
+            )
+            print(
+                "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
+                    epoch,
+                    batch_idx * len(data),
+                    len(train_loader.dataset),
+                    100.0 * batch_idx / len(train_loader),
+                    loss.item(),
+                )
+            )
+
+
+def test(args, model, device, test_loader, epoch):
+    model.eval()
+    test_loss = 0
+    correct = 0
+    with torch.no_grad():
+        for data, target in test_loader:
+            data, target = data.to(device), target.to(device)
+            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)
+
+    Logger.current_logger().report_scalar(
+        "test", "loss", iteration=epoch, value=test_loss
+    )
+    Logger.current_logger().report_scalar(
+        "test", "accuracy", iteration=epoch, value=(correct / len(test_loader.dataset))
+    )
+    print(
+        "Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)".format(
+            test_loss,
+            correct,
+            len(test_loader.dataset),
+            100.0 * correct / len(test_loader.dataset),
+        )
+    )
+
+
+def main(_):
+    # Connecting ClearML with the current process,
+    # from here on everything is logged automatically
+    task = Task.init(project_name="examples", task_name="pytorch mnist train with abseil")
+
+    use_cuda = FLAGS.cuda and torch.cuda.is_available()
+
+    torch.manual_seed(FLAGS.seed)
+
+    device = torch.device("cuda" if use_cuda else "cpu")
+
+    kwargs = {"num_workers": 4, "pin_memory": True} if use_cuda else {}
+    train_loader = torch.utils.data.DataLoader(
+        datasets.MNIST(
+            os.path.join("..", "data"),
+            train=True,
+            download=True,
+            transform=transforms.Compose(
+                [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
+            ),
+        ),
+        batch_size=FLAGS.batch_size,
+        shuffle=True,
+        **kwargs
+    )
+    test_loader = torch.utils.data.DataLoader(
+        datasets.MNIST(
+            os.path.join("..", "data"),
+            train=False,
+            transform=transforms.Compose(
+                [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
+            ),
+        ),
+        batch_size=FLAGS.test_batch_size,
+        shuffle=True,
+        **kwargs
+    )
+
+    model = Net().to(device)
+    optimizer = optim.SGD(model.parameters(), lr=FLAGS.lr, momentum=FLAGS.momentum)
+
+    for epoch in range(1, FLAGS.epochs + 1):
+        train(FLAGS, model, device, train_loader, optimizer, epoch)
+        test(FLAGS, model, device, test_loader, epoch)
+
+    if FLAGS.save_model:
+        torch.save(model.state_dict(), os.path.join(gettempdir(), "mnist_cnn.pt"))
+
+
+if __name__ == "__main__":
+    app.run(main)