Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/Mojo-Numerics-and-Algorithms-group/NuMojo

Last synced: about 2 months ago
JSON representation

Host: GitHub
URL: https://github.com/Mojo-Numerics-and-Algorithms-group/NuMojo
Owner: Mojo-Numerics-and-Algorithms-group
License: mit
Created: 2023-07-22T22:10:06.000Z (over 1 year ago)
Default Branch: main
Last Pushed: 2024-06-01T22:35:05.000Z (8 months ago)
Last Synced: 2024-06-01T23:50:22.416Z (8 months ago)
Language: Mojo
Size: 207 KB
Stars: 44
Watchers: 2
Forks: 8
Open Issues: 0
Metadata Files:
- Readme: README.MD
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Roadmap: Roadmap.md

Awesome Lists containing this project

awesome-mojo - NuMojo - A library for numerical computing in Mojo 🔥 similar to NumPy, SciPy in Python. (🗂️ Libraries / Math)
awesome-mojo-max-mlir - Mojo-Numerics-and-Algorithms-group/NuMojo - Numerics-and-Algorithms-group/NuMojo?style=social"/> : A numerics library for the Mojo programming language. (Scientific Computation)
awesome-mojo-max-mlir - Mojo-Numerics-and-Algorithms-group/NuMojo - Numerics-and-Algorithms-group/NuMojo?style=social"/> : A numerics library for the Mojo programming language. (Scientific Computation)

README

        



  

    

  

  
NuMojo


  


    NuMojo is a library for numerical computing in Mojo 🔥 similar to NumPy, SciPy in Python.

    


    

    



        Explore the docs»     

        Changelog»     

        Check out our Discord» 

    

    


    

        中文·简»    

        中文·繁»   

        日本語» 

    

    

    

    

    

  



  Table of Contents

  


    

      About The Project

      

         What NuMojo is 

        What NuMojo is not

      

    

    Goals/Roadmap

      

        Long term goals

      

    Usage

    How to install

    Contributing

    Warnings

    License

    Acknowledgments

  


## About the project

### What NuMojo is

NuMojo intends to capture a wide swath of numerics capability present in the Python packages NumPy, SciPy and Scikit.

NuMojo intends to try and get the most out of the capabilities of Mojo including vectorization, parallelization, and GPU acceleration(once available). Currently, NuMojo extends (most of) the standard library math functions to work on array inputs.

We intend NuMojo to be a building block for other Mojo packages that need fast math under the hood without the added weight of a ML back and forward propagation system

### What NuMojo is not

NuMojo is not a machine learning library, it will never include back-propagation in the base library.

## Goals / Roadmap

For a detailed roadmap, please refer to the [Roadmap.md](Roadmap.md) file.

Our main goal is to implement a fast, comprehensive numerics library in Mojo. Following are some brief long-term goals,

### Long term goals

* Linear Algebra

  * Native n-dimensional array types

  * Vectorized, Parallelized math operations

  * Array manipulation - vstack, slicing, concat etc.

* Calculus

  * Integration & Derivatives etc

* Optimizers

* Function approximators

* Sorting

## Usage

An example goes as follows.

```mojo

import numojo as nm

from numojo.prelude import *

fn main() raises:

    # Generate two 1000x1000 matrices with random float64 values

    var A = nm.random.randn[f64](shape=List[Int](1000, 1000))

    var B = nm.random.randn[f64](shape=List[Int](1000, 1000))

    # Generate a 3x2 matrix from string representation

    var X = nm.fromstring[f32]("[[1.1, -0.32, 1], [0.1, -3, 2.124]]")

    # Print array

    print(A)

    # Array multiplication

    var C = A @ B

    # Array inversion

    var I = nm.inv(A)

    # Array slicing

    var A_slice = A[1:3, 4:19]

    # Get scalar from array

    var A_item = A.item(291, 141)

```

Please find all the available functions [here](features.md)

## How to install

There are two approach to install and use the Numojo package.

### Build package

This approach invovles building a standalone package file `mojopkg`.

1. Clone the repository.

2. Build the package using `mojo package numojo`

3. Move the numojo.mojopkg into the directory containing the your code.

### Include NuMojo's path for compiler and LSP

This approach does not require buiding a package file. Instead, when you compile your code, you can include the path of NuMojo reporsitory with the following command:

```console

mojo run -I "../NuMojo" example.mojo

```

This is more flexible as you are able to edit the NuMojo source files when testing your code.

In order to allow VSCode LSP to resolve the imported `numojo` package, you can:

1. Go to preference page of VSCode.

2. Go to `Mojo › Lsp: Include Dirs`

3. Click `add item` and write the path where the Numojo repository is located, e.g. `/Users/Name/Programs/NuMojo`.

4. Restart the Mojo LSP server.

Now VSCode can show function hints for the Numojo package!

## Contributing

Any contributions you make are **greatly appreciated**. For more details and guidelines on contributions, please check [here](CONTRIBUTING.md)

## Warnings

This library is still very much a work in progress and may change at any time.

## License

Distributed under the Apache 2.0 License with LLVM Exceptions. See [LICENSE](https://github.com/Mojo-Numerics-and-Algorithms-group/NuMojo/blob/main/LICENSE) and the LLVM [License](https://llvm.org/LICENSE.txt) for more information.

## Acknowledgements

* Built in native [Mojo](https://github.com/modularml/mojo) which was created by [Modular](https://github.com/modularml)