Shumai (Meta)

Shumai, created by facebookresearch, is a fast and efficient differentiable tensor library for JavaScript and TypeScript. Leveraging Bun + Flashlight, Shumai is ideal for both software engineers and researchers.

Features

• High-Performance Computing: Utilizes Bun + Flashlight for optimized tensor operations, providing speed and efficiency on par with native solutions.
• Cross-Platform Support: Works seamlessly on MacOS and Linux, with experimental support for Windows via Docker + WSL2.
• Fast Differentiation: Supports automatic differentiation, enabling the quick creation and training of machine learning models.
• Easy Integration: Native typed arrays and JIT compiler make data manipulation simple and efficient, enhancing application development.
• Active Development: Open-source and MIT licensed, with an ongoing commitment to improvements and community contributions.

Use Cases:

• Machine Learning Model Training: Train small to medium-sized machine learning models with fast FFI bindings and expressive syntax.
• Data Preparation: Manipulate data effortlessly in JavaScript, preparing it for complex computations and training.
• Scientific Research: Conduct research with powerful tensor computations, leveraged efficiently on both CPU and GPU architectures.
• Application Development: Build applications with robust numerical computations backed by Shumai's high performance tensor operations.

Shumai stands out as a premier choice for developers and researchers needing a robust and performant differentiable tensor library in the JavaScript ecosystem, enhanced by the power of Bun + Flashlight.