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Readme

🌀 Wrapture

One-click exporter from PyTorch models to Web-ready ONNX with JS/TS wrappers.

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About

Wrapture lets you go from a Python-trained model to deployable JavaScript with a single command. It generates TypeScript bindings and a Web/Node-compatible wrapper, using WebGPU/WASM-ready ONNX runtimes.

[!NOTE] This is an experiment trying to fulfil a need between python and js. YMMV

🌀 Wrapture

🚀 Features

✅ Convert PyTorch models to ONNX
✅ Optional ONNX simplification and quantization
✅ Generate loadModel() + predict() JavaScript wrappers
✅ Auto-generate .d.ts TypeScript bindings

Prerequisites

Python 3.10+ required

Install Python if you don’t have it: 👉 https://www.python.org/downloads/

Install required Python packages

python3 -m pip install torch onnx onnxsim onnxruntime

Check your installation:

python3 -c "import torch; print(torch.__version__)"
python3 -c "import onnx; print(onnx.__version__)"

You should see output like:

2.x.x etc..

Installation

npm i -g wrapture

Generating a Model

A helper script is provided to create a basic test model.

python3 python/scripts/basic_model.py

This generates:

test/fixtures/basic_model.pt

Usage

wrapture --input test/fixtures/basic_model.pt --output ./wrapped

You’ll see a spinner as the model is converted, and then a JS/TS wrapper is written to the ./wrapped/ directory.

Output Structure

Example contents of a --output ./ folder:

/
 ├── wrapped.ts # The loadModel() + predict() logic
 ├── wrapped.d.ts # Fully typed API
 └── model.onnx # Exported ONNX model

Example: Using the Generated Model

import { loadModel } from './wrapped.js';

const model = await loadModel();

const input = { data: new Float32Array(1 _3_ 224 \* 224), dims: [1, 3, 224, 224]
};

const result = await model.predict(input); console.log(result); // { // logits:
Float32Array, // probabilities: number[], // predictedClass: number // }

API

Full API documentation is available here.

Contributing

Want to contribute? Please read the CONTRIBUTING.md and CODE_OF_CONDUCT.md

License

This project is licensed under the MIT License - see the LICENSE file for details.

Changelog

See the CHANGELOG.md for details on the latest updates.

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