| Name | torchviz2 JSON |
| Version |
0.0.2
JSON |
| download |
| home_page | None |
| Summary | A small package to create visualizations of PyTorch execution graphs |
| upload_time | 2024-09-28 15:22:49 |
| maintainer | None |
| docs_url | None |
| author | Leo Ware |
| requires_python | None |
| license | MIT |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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|
PyTorchViz
==========
**This is a fork of the original package `torchviz`, which is no longer maintained.**
A small package to create visualizations of PyTorch execution graphs and traces.
## Installation
Install graphviz, e.g.:
```
brew install graphviz
```
Install the package itself:
```
pip install torchviz2
```
## Usage
Example usage of `make_dot`:
```
import torch
from torch import nn
from torchviz import 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)
```
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