https://github.com/szagoruyko/pytorchviz

A small package to create visualizations of PyTorch execution graphs
https://github.com/szagoruyko/pytorchviz

Last synced: 7 days ago
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A small package to create visualizations of PyTorch execution graphs

• Host: GitHub
• URL: https://github.com/szagoruyko/pytorchviz
• Owner: szagoruyko
• License: mit
• Created: 2018-01-30T15:37:55.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2024-12-30T21:14:55.000Z (about 2 months ago)
• Last Synced: 2025-02-07T14:00:35.586Z (14 days ago)
• Language: Jupyter Notebook
• Size: 144 KB
• Stars: 3,291
• Watchers: 31
• Forks: 280
• Open Issues: 34
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
PyTorchViz

=======

A small package to create visualizations of PyTorch execution graphs and traces.

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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()))

```

![image](https://user-images.githubusercontent.com/13428986/110844921-ff3f7500-8277-11eb-912e-3ba03623fdf5.png)

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)

```

![image](https://user-images.githubusercontent.com/13428986/110845186-4ded0f00-8278-11eb-88d2-cc33413bb261.png)

## 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).