Shumai: Revolutionizing Tensor Libraries in JavaScript and TypeScript
Shumai is a remarkable fast, network-connected, differentiable tensor library for TypeScript (and JavaScript). It is built with bun + flashlight, making it an ideal choice for software engineers and researchers.
Overview
Shumai offers a seamless experience with its ability to handle various tasks. It is designed to make creating datasets easier, training small models faster, and enabling more expressive advanced/fine-grained training and inference logic. The library also leverages the large ecosystem of JavaScript to facilitate better application development.
Core Features
- Fast Performance: Shumai demonstrates impressive benchmark results. On an Apple M1 Pro and an Nvidia GP100, it outperforms TF.js in several operations, such as addition and matrix multiplication.
- Memory Management: Users can tune memory usage to improve performance by reducing garbage collector overhead.
- Statistics Gathering: It provides options for basic and advanced statistics collection, including across multiple threads, processes, and hosts.
Basic Usage
- Installation: The installation process is a work in progress, but detailed instructions are provided for macOS and Linux users. Prerequisites include having bun and ArrayFire installed.
- Usage Examples: Shumai comes with clear usage examples, including creating tensors, performing operations, and converting between tensors and native JavaScript arrays. It also supports gradients for more complex operations.
In conclusion, facebookresearch/shumai is a powerful tool that combines the best of JavaScript and AI to offer a unique and efficient solution for various tasks.
