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VisualTorch aims to help visualize Torch-based neural network architectures.
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| 2026-07-31 | 314 |
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<div align="center"> <img alt="VisualTorch" height="70" src="https://raw.githubusercontent.com/willyfh/visualtorch/main/docs/source/_static/images/logos/wordmark-light.png" /> <br/> <br/> []() []() [](https://pepy.tech/project/visualtorch) [](https://github.com/willyfh/visualtorch/actions/workflows/pytest.yml) [](https://visualtorch.readthedocs.io/en/latest/?badge=latest) </div> **VisualTorch** aims to help visualize Torch-based neural network architectures. It currently supports generating flow-style, graph-style, and LeNet-style architectures for PyTorch Sequential and Custom models. Its original visual styles were inspired by [visualkeras](https://github.com/paulgavrikov/visualkeras), [pytorchviz](https://github.com/szagoruyko/pytorchviz), [pytorch-summary](https://github.com/sksq96/pytorch-summary), and [torchview](https://github.com/mert-kurttutan/torchview); since then, it has grown its own unified tracing backend and architecture-handling logic well beyond its origins. **Note:** `1.0+` is a major release with breaking API changes, but with significantly better features and algorithms - upgrading is recommended. For the old API, use `0.2.5` or older. **Limitation:** VisualTorch traces a real forward pass to build the diagram, which has an inherent limitation shared by any tracing-based approach (not a bug, and not fixable without full symbolic execution): models with **data-dependent control flow** (e.g. a branch only taken if a tensor value crosses some threshold) only show whichever branch the traced dummy input happened to take. Separately, a layer that returns **multiple meaningful output tensors** (e.g. a custom multi-task head, or `nn.LSTM`'s `(output, (h_n, c_n))`) still has its node's size based on only its first tensor; with `show_dimension=True`, every output tensor's shape is shown in the label, not just the first. Downstream connections are correct either way. Contributions are welcome! <div align="center">  </div> ### Animated Reveal Every style can also render as an animated GIF, revealing the model one layer/column at a time, via `visualtorch.animate(model, input_shape, style=...)` - see it in action for [flow](https://visualtorch.readthedocs.io/en/latest/usage_examples/flow/plot_animated_reveal_flow.html), [graph](https://visualtorch.readthedocs.io/en/latest/usage_examples/graph/plot_animated_reveal_graph.html), and [lenet](https://visualtorch.readthedocs.io/en/latest/usage_examples/lenet_style/plot_animated_reveal_lenet_style.html) styles. <div align="center">  </div> ## Documentation Online documentation is available at [visualtorch.readthedocs.io](https://visualtorch.readthedocs.io/en/latest/). The docs include [usage examples](https://visualtorch.readthedocs.io/en/latest/usage_examples/index.html), [API references](https://visualtorch.readthedocs.io/en/latest/markdown/api_references/index.html), and other useful information. ## Installation See the [Installation page](https://visualtorch.readthedocs.io/en/latest/markdown/get_started/installation.html). ## MCP integration VisualTorch includes an optional, client-neutral stdio MCP server for generating static PNG
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:fef4c0e1c8c78405, topic:deep-learning, topic:neural-network, topic:pytorch