PyTorchViz
==========
A small package to create visualizations of PyTorch execution graphs and traces.
[](https://colab.research.google.com/github/szagoruyko/pytorchviz/blob/master/examples.ipynb)
## Installation
Install graphviz, e.g.:
```
brew install graphviz
```
Install the package itself:
```
pip install torchviz
```
## Usage
Example usage of `make_dot`:
```
model = nn.Sequential()
model.add_module('W0', nn.Linear(8, 16))
model.add_module('tanh', nn.Tanh())
model.add_module('W1', nn.Linear(16, 1))
x = torch.randn(1, 8)
y = model(x)
make_dot(y.mean(), params=dict(model.named_parameters()))
```
Set `show_attrs=True` and `show_saved=True` to see what autograd saves for the backward pass. (Note that this is only available for pytorch >= 1.9.)
```
model = nn.Sequential()
model.add_module('W0', nn.Linear(8, 16))
model.add_module('tanh', nn.Tanh())
model.add_module('W1', nn.Linear(16, 1))
x = torch.randn(1, 8)
y = model(x)
make_dot(y.mean(), params=dict(model.named_parameters()), show_attrs=True, show_saved=True)
```
## Acknowledgements
The script was moved from [functional-zoo](https://github.com/szagoruyko/functional-zoo) where it was created with the help of Adam Paszke, Soumith Chintala, Anton Osokin, and uses bits from [tensorboard-pytorch](https://github.com/lanpa/tensorboard-pytorch).
Other contributors are [@willprice](https://github.com/willprice), [@soulitzer](https://github.com/soulitzer), [@albanD](https://github.com/albanD).
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