diff --git a/requirements.test.txt b/requirements.test.txt
index abf5036..cb27d72 100644
--- a/requirements.test.txt
+++ b/requirements.test.txt
@@ -1 +1,2 @@
-torchvision
\ No newline at end of file
+torchvision
+clearml
\ No newline at end of file
diff --git a/tests/test_train_mnist_clearml.py b/tests/test_train_mnist_clearml.py
new file mode 100644
index 0000000..ab73ca4
--- /dev/null
+++ b/tests/test_train_mnist_clearml.py
@@ -0,0 +1,88 @@
+import os
+from dataclasses import dataclass
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.utils.data import DataLoader
+from torchvision import transforms
+from torchvision.datasets import MNIST
+
+from trainer import Trainer, TrainerArgs, TrainerConfig, TrainerModel
+
+is_cuda = torch.cuda.is_available()
+
+
+@dataclass
+class MnistModelConfig(TrainerConfig):
+    optimizer: str = "Adam"
+    lr: float = 0.001
+    epochs: int = 1
+    print_step: int = 1
+    plot_step: int = 5
+    save_step: int = 30
+    dashboard_logger: str = "clearml"
+    project_name: str = "pytorch-mnist-clearml"
+    run_name: str = "test-run"
+
+
+class MnistModel(TrainerModel):
+    def __init__(self):
+        super().__init__()
+
+        # mnist images are (1, 28, 28) (channels, height, width)
+        self.layer_1 = nn.Linear(28 * 28, 128)
+        self.layer_2 = nn.Linear(128, 256)
+        self.layer_3 = nn.Linear(256, 10)
+
+    def forward(self, x):
+        batch_size, _, _, _ = x.size()
+
+        # (b, 1, 28, 28) -> (b, 1*28*28)
+        x = x.view(batch_size, -1)
+        x = self.layer_1(x)
+        x = F.relu(x)
+        x = self.layer_2(x)
+        x = F.relu(x)
+        x = self.layer_3(x)
+
+        x = F.log_softmax(x, dim=1)
+        return x
+
+    def train_step(self, batch, criterion):
+        x, y = batch
+        logits = self(x)
+        loss = criterion(logits, y)
+        return {"model_outputs": logits}, {"loss": loss}
+
+    def eval_step(self, batch, criterion):
+        x, y = batch
+        logits = self(x)
+        loss = criterion(logits, y)
+        return {"model_outputs": logits}, {"loss": loss}
+
+    @staticmethod
+    def get_criterion():
+        return torch.nn.NLLLoss()
+
+    def get_data_loader(
+        self, config, assets, is_eval, samples, verbose, num_gpus, rank=0
+    ):  # pylint: disable=unused-argument
+        transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
+        dataset = MNIST(os.getcwd(), train=not is_eval, download=True, transform=transform)
+        dataset.data = dataset.data[:256]
+        dataset.targets = dataset.targets[:256]
+        mnist_train = DataLoader(dataset, batch_size=8)
+        return mnist_train
+
+
+def test_train_mnist():
+    model = MnistModel()
+    trainer = Trainer(
+        TrainerArgs(), MnistModelConfig(), model=model, output_path=os.getcwd(), gpu=0 if is_cuda else None
+    )
+    trainer.fit()
+
+
+if __name__ == "__main__":
+    test_train_mnist()