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torchvista

(★ 759)

Interactive Pytorch forward pass visualization in notebooks

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Download Latest Version (.zip)

- - collapsible-graph.gif
  - demo.gif
  - error-graph.png
  - info-popup.png
  - interactive-graph.gif
- - tutorial.html
- demos.html
- index.html
- tutorials.html
- examples_generator.py
- find_function_mode_overrides.py
- __init__.py
- _helpers.py
- test_build_immediate_ancestor_map.py
- test_extract_tensors_from_obj.py
- test_process_graph.py
- test_transform_to_nested_graph.py
- test_validate_export_format.py
- - __init__.py
  - d3.min.js
  - jsoneditor-10.2.0.min.css
  - jsoneditor-10.2.0.min.js
  - viz-standalone.js
- - __init__.py
  - demo-graph.html
  - graph.html
- __init__.py
- engine.py
- enums.py
- graph_transforms.py
- module_discovery.py
- overrides.py
- render.py
- tensor_utils.py
- tracer.py
.gitignore
LICENSE
NOTICE.txt
README.md
requirements.txt
setup.py

# Installation Guide

1. Get the code

git clone https://github.com/sachinhosmani/torchvista

Downloads the entire project code from GitHub to your computer.

cd torchvista

Moves into the project folder you just downloaded.

2. Official Install Script

Easy Recommended

Prerequisites

Python 3 Python is required to use pip.

pip install torchvista

Installs the package published on PyPI directly — no need to clone the source.

✅ After installing, open a new terminal and run the program's version command (e.g. --version) to confirm it worked.

Pulled directly from this repo's README.

3. Python

Easy

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install torchvista

Installs the package published on PyPI directly — no need to clone the source.

pip install -e ".[test]"

Installs the Python libraries listed in requirements.txt (or similar).

✅ If it runs without errors and prints output in the terminal, it worked.

Pulled directly from this repo's README.

// repository documentation

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# torchvista

An interactive tool to visualize the forward pass of a PyTorch model directly in the notebook—with a single line of code. Works with web-based notebooks like Jupyter, Google Colab and Kaggle. Also allows you to export the visualization as image, svg and HTML.

## ✨ Features

### Interactive graph with drag and zoom support

![](https://raw.githubusercontent.com/sachinhosmani/torchvista/main/docs/assets/interactive-graph.gif)

--------

### Collapsible nodes for hierarchical modules

![](https://raw.githubusercontent.com/sachinhosmani/torchvista/main/docs/assets/collapsible-graph.gif)

--------

### Error-tolerant partial visualization when errors arise
(e.g., shape mismatches) for ease of debugging

![](https://raw.githubusercontent.com/sachinhosmani/torchvista/main/docs/assets/error-graph.png)

--------

### Click on nodes to view parameter and attribute info

![](https://raw.githubusercontent.com/sachinhosmani/torchvista/main/docs/assets/info-popup.png)

--------

## Tutorials and examples

- Step-by-step tutorial 👉 [here](https://sachinhosmani.github.io/torchvista/tutorials.html)
- Quick Google Colab tutorial 👉 [here](https://colab.research.google.com/drive/1wrWKhpvGiqHhE0Lb1HnFGeOcS4uBqGXw?usp=sharing) (must be logged in to Colab)
- Check out demos 👉 [here](https://sachinhosmani.github.io/torchvista/demos.html)

## How does it work?

I wrote a technical deep-dive blog post on Towards Data Science which can be read [here](https://towardsdatascience.com/torchvista-building-an-interactive-pytorch-visualization-package-for-notebooks/). It explains how the tool works under the hood.

## ⚙️ Usage

Install via pip
```
pip install torchvista
```

Alternatively, install via `conda` (See [`torchvista-feedstock`](https://github.com/conda-forge/torchvista-feedstock?tab=readme-ov-file#installing-torchvista) for more information)

```
conda install -c conda-forge torchvista
```

Run from your **web-based notebook** (Jupyter, Colab, VSCode notebook, etc)

```
import torch
import torch.nn as nn

# Import torchvista
from torchvista import trace_model

# Define your module
class LinearModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(10, 5)

def forward(self, x):
        return self.linear(x)

# Instantiate the module and tensor input
model = LinearModel()
inputs = torch.randn(2, 10)
model.eval() # Optional

# Trace!
trace_model(model, inputs)
```
## API: `trace_model`

```python
trace_model(
    model,
    inputs,
    show_non_gradient_nodes=True,
    collapse_modules_after_depth=1,
    forced_module_tracing_depth=None,
    height=800,
    width=None,
    export_format=None,
    show_module_attr_names=False,
    export_path=None,
    show_compressed_view=False,
)
```

| Parameter | Type | Default Value | Category | Description |
| --- | --- | --- | --- | --- |
| `model` | `torch.nn.Module` | — | Tracing | Model instance to visualize. |
| `inputs` | `Any` | — | Tracing | Input(s) forwarded into the model; pass a single input or a tuple. |
| `show_non_gradient_nodes` | `bool` | `True` | Visual | Display nodes for constants and other values outside the gradient graph. |
| `collapse_modules_after_depth` | `int` | `1` | Visual | Depth to initially expand nested modules; `0` collapses everything (nodes can still be expanded interactively). |
| `forced_module_tracing_depth` | `int` | `None` | Tracing | Maximum depth of module internals to trace; `None` traces only user-defined modules. |
| `height` | `int` | `800` | Visual | Canvas height in pixels. |
| `width` | `int \| str` | `None` | Visual | Canvas width; accepts pixels or percentages; defaults to full available width when omitted. |
| `export_format` | `str` | `None` | Export | Optional export format: `png`, `svg`, or `html` if exporting graph as a file. Otherwise, by default the graph is shown within the notebook.|
| `show_module_attr_names` | `bool` | `False` | Visual | Display attribute names for modules when available instead of just class names. |
| `export_path` | `str` | `None` | Export | Custom path if exporting as a file. **Only HTML format** is currently supported with custom export paths. If only file name is specified, it will be created inside the present working directory. |
| `show_compressed_view` (Experimental) | `bool` | `False` | Visual | Compress the graph by showing repeating nodes of the same type with identical input and output dims in single "repeat" blocks. This feature currently only recognises repeating nodes within `Sequential` and `ModuleList`. WARNING: this feature might be expensive on large models. |

## Running tests

Tests live under [`tests/`](tests/) and use [pytest](https://docs.pytest.org/).

```bash
pip install -e ".[test]"
pytest
```