--- title: TensorBoardX with PyTorch --- The [pytorch_tensorboardX.py](https://github.com/clearml/clearml/blob/master/examples/frameworks/tensorboardx/pytorch_tensorboardX.py) example demonstrates the integration of ClearML into code that uses PyTorch and TensorBoardX. The 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 with tensorboardX` in the `examples` project * ClearML automatically captures scalars and text logged using the TensorBoardX `SummaryWriter` object, and the model created by PyTorch ## Scalars The loss and accuracy metric scalar plots appear in the task's page in the **ClearML web UI**, under **SCALARS**. The also includes resource utilization plots, which are titled **:monitor: machine**.   ## 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's 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.  