Shumai (Meta)
Shumai (Meta)
Fast, network-connected, differentiable tensor library for TypeScript/JS.
Pricing
Free
New Features
Open Source
Tool Info
Rating: N/A (0 reviews)
Date Added: November 23, 2022
Categories
Developer Tools
Description
Shumai is a open-source tensor library designed for use with TypeScript and JavaScript. It is a fast and network-connected library that is ideal for software engineers and researchers who need to work with complex data sets and perform advanced calculations. Shumai is built with bun and flashlight, two powerful tools that help to ensure its speed and reliability. With Shumai, users can perform a wide range of operations on tensors, including addition, subtraction, multiplication, and division. The library also supports a variety of differentiable functions.
Key Features
- Open-source tensor library
- Built with bun and flashlight for speed and reliability
- Supports a wide range of tensor operations
- Supports differentiable functions for machine learning and advanced applications
- Ideal for software engineers and researchers working with complex data sets
Use Cases
- Machine learning researchers and practitioners who need a high-performance tensor library for their TypeScript or JavaScript projects
- Software engineers who are building machine learning applications and need a library that can handle large-scale tensor operations
- Data scientists who need to manipulate and analyze large datasets using tensors
- Academic institutions and research labs that require a reliable and efficient tensor library for their machine learning projects
- Creating datasets easier: JavaScript, with native typed arrays and a JIT compiler, is perfect for twiddling with data before it can be made into big, flat GPU-compatible arrays.
- Training small models faster: FFI bindings in Bun are crazy fast (~3ns), so JS gets out of the way when training small models:
- Advanced/fine-grained training/inference logic more expressive: Bun uses the JSC JIT compiler, meaning you can confidently write complex training logic without needing a native C++ implementation
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