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This project simplifies the process of deploying machine learning models trained in Python to JavaScript environments. With just a single command, you can generate TypeScript bindings and create a Web/Node-compatible wrapper using ONNX runtimes that are ready for WebGPU and WASM.\n\n## Table of Contents\n\n- [Features](#features)\n- [Getting Started](#getting-started)\n- [Installation](#installation)\n- [Usage](#usage)\n- [Topics](#topics)\n- [Contributing](#contributing)\n- [License](#license)\n- [Releases](#releases)\n- [Contact](#contact)\n\n## Features\n\n- **One Command Deployment**: Transition from Python to JavaScript effortlessly.\n- **TypeScript Bindings**: Automatically generate TypeScript bindings for your models.\n- **Web/Node Compatibility**: Create wrappers that work seamlessly in both web and Node.js environments.\n- **WebGPU and WASM Ready**: Utilize the latest technologies for efficient model execution.\n- **Support for ONNX**: Leverage the ONNX runtime for model inference.\n\n## Getting Started\n\nTo get started with Wrapture, follow these steps:\n\n1. **Install Dependencies**: Ensure you have the necessary tools installed.\n2. **Prepare Your Model**: Train your model in Python and export it in ONNX format.\n3. **Run Wrapture**: Use the command line to generate your JavaScript deployment.\n\n## Installation\n\nYou can install Wrapture using npm. Open your terminal and run:\n\n```bash\nnpm install wrapture\n```\n\n## Usage\n\nAfter installing, you can deploy your model with a single command. Here’s how:\n\n1. **Export Your Model**: Ensure your model is exported as an ONNX file.\n2. **Run Wrapture**: Use the following command:\n\n```bash\nwrapture deploy path/to/your/model.onnx\n```\n\nThis command will generate the necessary TypeScript bindings and wrappers.\n\n## Topics\n\nWrapture covers a range of topics relevant to modern machine learning and deployment:\n\n- JavaScript\n- Machine Learning\n- Model Conversion\n- ONNX\n- PyTorch\n- Quantization\n- Simplifier\n- TypeScript\n- WASM\n- WebGPU\n\n## Contributing\n\nWe welcome contributions! If you want to contribute to Wrapture, please follow these steps:\n\n1. Fork the repository.\n2. Create a new branch for your feature or bug fix.\n3. Make your changes and commit them.\n4. Push your branch to your forked repository.\n5. Open a pull request.\n\n## License\n\nWrapture is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.\n\n## Releases\n\nTo check for the latest releases, visit the [Releases](https://github.com/robertocenteno/wrapture/releases) section. You can download the latest version and execute it to start deploying your models.\n\n## Contact\n\nFor any inquiries or feedback, feel free to reach out:\n\n- **GitHub**: [Wrapture GitHub](https://github.com/robertocenteno/wrapture)\n- **Email**: your-email@example.com\n\n---\n\nThank you for checking out Wrapture! We hope this tool simplifies your model deployment process. For more details, visit the [Releases](https://github.com/robertocenteno/wrapture/releases) section.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frobertocenteno%2Fwrapture","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frobertocenteno%2Fwrapture","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frobertocenteno%2Fwrapture/lists"}