Shumai is an open-source project described as a fast differentiable tensor library built using JavaScript, TypeScript, and Bun and Flashlight. It aims to facilitate machine learning tasks by providing developers a comprehensive set of tools and libraries. Users can access documentation, issues, activity updates, and other resources related to the project through the GitHub platform.
⚡Top 5 Shumai Features:
- Fast Differentiable Tensor Library: Designed to be a fast, network-connected, differentiable tensor library for TypeScript and JavaScript.
- Built with Bun + Flashlight: It is built using Bun, a modern bundler, and Flashlight, a machine learning framework.
- Supports macOS and Linux: Supports both macOS and Linux operating systems.
- GPU Computation Support: It supports GPU computation using CUDA for Linux users and CPU computation for macOS users.
- Gradient Calculations: Supports gradient calculations, allowing for automatic differentiation during training.
⚡Top 5 Shumai Use Cases:
- Creating Datasets: Shumai aims to make creating datasets easier.
- Training Small Models Faster: It is designed to train small models faster than other libraries.
- Advanced Training/Inference Logic: Advanced and fine-grained training/inference logic, making it more expressive for developers.
- Bun’s JIT Compiler: The library leverages Bun’s Just-In-Time (JIT) compiler, which allows for complex training logic without the need for a native C++ implementation.
- Large Ecosystem Support: JavaScript has a large ecosystem that facilitates better application development.
