# Tutorial: Tensorboard in Pytorch

Follow Tutorial here

{% embed url="<https://github.com/yunjey/pytorch-tutorial/tree/master/tutorials/04-utils/tensorboard>" %}

## TensorBoard in PyTorch

In this tutorial, we implement a MNIST classifier using a simple neural network and visualize the training process using [TensorBoard](https://www.tensorflow.org/get_started/summaries_and_tensorboard). In training phase, we plot the loss and accuracy functions through `scalar_summary` and visualize the training images through `image_summary`. In addition, we visualize the weight and gradient values of the parameters of the neural network using `histogram_summary`. PyTorch code for handling these summary functions can be found [here](https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/04-utils/tensorboard/main.py#L81-L97).

![](https://github.com/yunjey/pytorch-tutorial/raw/master/tutorials/04-utils/tensorboard/gif/tensorboard.gif)

<br>

### Usage

**1. Install the dependencies**

```
$ pip install -r requirements.txt
```

**2. Train the model**

```
$ python main.py
```

**3. Open the TensorBoard**

To run the TensorBoard, open a new terminal and run the command below. Then, open <http://localhost:6006/> on your web browser.

```
$ tensorboard --logdir='./logs' --port=6006
```