**Shumai** is a fast, network-connected, differentiable tensor library designed for **JavaScript** and **TypeScript** developers. Built with **Bun** and **Flashlight**, it’s perfect for both software engineers and researchers who need efficient tensor operations. Whether you're working on machine learning models or data processing, Shumai has got you covered.

What are the features of Shumai?

* **Fast Tensor Operations**: Optimized for speed, Shumai outperforms other libraries in benchmarks. * **GPU Acceleration**: Automatically leverages GPUs for faster computations. * **Differentiable Tensors**: Supports gradient calculations for machine learning tasks. * **Easy Integration**: Works seamlessly with JavaScript and TypeScript projects. * **Cross-Platform**: Supports macOS and Linux, with experimental Windows support via Docker.

What are the use cases of Shumai?

* **Machine Learning**: Train small models faster with Shumai’s efficient tensor operations. * **Data Processing**: Manipulate and transform data using native JavaScript arrays. * **Research**: Experiment with advanced training and inference logic without needing C++.

* **Install Bun**: Run `curl https://bun.sh/install | bash` to install Bun. * **Install ArrayFire**: Use Homebrew for macOS (`brew install arrayfire`) or follow Linux instructions. * **Install Shumai**: Run `bun install @shumai/shumai` to get started. * **Basic Usage**: Import Shumai and start creating tensors, performing operations, and calculating gradients.

Does Shumai support Windows?

Officially, Shumai supports macOS and Linux, but Windows users can use Docker + WSL2 for compatibility.

Can I use Shumai for large-scale machine learning?

Shumai is optimized for small to medium-sized models. For large-scale tasks, consider other frameworks.

How does Shumai compare to TensorFlow.js?

Shumai is faster in many benchmarks, especially for tensor operations and GPU-accelerated tasks.

Shumai

5.0

19.25%

Shumai is a powerful tensor library for JavaScript and TypeScript, offering fast, GPU-accelerated operations and easy integration for machine learning and data processing tasks.

Added on: 2025.03.04

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