---
title: PyTorch MNIST
---
The [pytorch_mnist.py](https://github.com/clearml/clearml/blob/master/examples/frameworks/pytorch/pytorch_mnist.py) example
demonstrates the integration of ClearML into code that uses PyTorch.
The example script does the following:
* Trains a simple deep neural network on the PyTorch built-in [MNIST](https://pytorch.org/vision/stable/datasets.html#mnist)
dataset.
* Creates a task named `pytorch mnist train` in the `examples` project.
* ClearML automatically logs `argparse` command line options, and models (and their snapshots) created by PyTorch.
* Additional metrics are logged by calling [`Logger.report_scalar()`](../../../references/sdk/logger.md#report_scalar).
## Scalars
In the example script's `train` function, the following code explicitly reports scalars to ClearML:
```python
Logger.current_logger().report_scalar(
"train", "loss", iteration=(epoch * len(train_loader) + batch_idx), value=loss.item()
)
```
In the `test` method, the code explicitly reports `loss` and `accuracy` scalars.
```python
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))
)
```
These scalars can be visualized in plots, which appear in the ClearML [web UI](../../../webapp/webapp_overview.md),
in the task's **SCALARS** tab.
## Hyperparameters
ClearML automatically logs command line options defined with `argparse`. They appear in **CONFIGURATION** **>** **HYPERPARAMETERS** **>** **Args**.
## Console
Text printed to the console for training progress, as well as all other console output, appear in **CONSOLE**.
## Artifacts
Models created by the task appear in the task's **ARTIFACTS** tab. ClearML automatically logs and tracks models
and any snapshots created using PyTorch.
Clicking on the model name takes you to the [model's page](../../../webapp/webapp_model_viewing.md), where you can view
the model's details and access the model.
